esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods
### Evaluation of Tissue Reaction  
To determine the tissue reaction to ESHF and AHPS, the areas in direct contact with the test materials were analyzed using a light stereomicroscope (LECO Corp., St. Joseph, MI, USA) coupled with a digital Sony Cyber-Shot DSC-W180 camera (Sony Corp, Tokyo, Japan). Photographs were taken at different magnifications from three arbitrarily selected sections representing the center of each specimen. The images were transferred to a computer and analyzed with the Image J 1.38 Ximage-analyzer software (National Institutes of Health, Bethesda, MD, USA). The tissue reaction was viewed at different magnifications on the three randomly selected sections limited to a predetermined area measuring 1000 \(\times\) 850 \(\upmu\)m located at the open end of the silicone tubes. The inflammatory reactions were scored according to the following parameters: none, presence of a fibrous capsule with new healthy bone trabeculae formation (NHBF) and absence of inflammatory cells; mild, fibrous capsule, NHBF and presence of few remaining inflammatory cells; moderate, presence of some concentration or clusters of polymorphonuclear leukocytes, lymphocytes, plasmocytes and macrophages without NHBF; severe, presence of large accumulations of polymorphonuclear leukocytes, lymphocytes, plasmocytes, macrophages, some foreign-body giant cellsand congested capillaries. Mean scores for ESHF and AHPS were calculated from the sum of the scores for all the parameters analyzed. All measurements were performed by two calibrated examiners who were blinded to the treatments. The sealers were considered biologically acceptable if the tissue reaction was scored as none or mild.
# Therapeutic Potential and Challenges of Pioglitazone in Cancer Treatment
## 3 Therapeutic Use of Pioglitazone
### Therapeutic Effect on Different Types of Cancer
#### 3.6.9 Pancreatic Cancer  
There has been contradictory evidence regarding the association between the antidiabetic use of pioglitazone and the elevation of the risk of pancreatic cancer. As such, the effect the molecule has on pancreatic cancer occurrence, and the extent of it, is yet to be determined [120; 121; 122; 123]. Nevertheless, antitumor activity has been observed in cell lines modeling pancreatic cancer, including Capan-1, Apsc-1 BxPC-3, PANC-1, and MIApaCa-2. Xenograft studies involving BxPC-3 revealed antiproliferative and antimetastatic properties, as tumor size was limited along with lymph metastasis [124]. Finally, pioglitazone has been reported to enhance the anticancer effects of gemcitabine through inhibition of the NF-\(\kappa\beta\), induction of apoptosis, and setting of the platform to include HDAC inhibitors that have proven to be helpful in the activity of PPAR-\(\gamma\), as illustrated in Figure 4[125].
# Artificial Intelligence-Based Autonomous UAV Networks: A Survey
## 5 Challenges and Open Research Areas
### MAC Protocol Design  
The use of the right MAC protocol is very important in achieving high efficiency for the UAV-incorporated networks. Many network researchers have proposed various techniques to improve the traditional MAC protocols to make them suitable for UAVs. However, to achieve high-efficiency requirements for the UAV networks, the MAC protocols should be designed specifically for UAVs. The cognitive radio approach can be used as a potential partner in designing MAC protocols for UAVs-assisted networks.
# Audio Fingerprint Extraction Based on Locally Linear Embedding for Audio Retrieval System
## 5 Conclusions  
A fingerprint dimensionality reduction method based on LLE and a fingerprint matching method based on DTW are introduced in this paper. Firstly, the energy vectors of sub-regions in the frequency domain is reduced which involved in fingerprint calculation. Then the reduction of the fingerprint is obtained by comparing the adjacent energies. Additionally, the matching segment is adjusted by the DTW algorithm, which improved the resistance to linear speed changes of the audio signal. The experimental results show that this novel method has good retrieval performance both in single group and multiple group reduction, and the maximum fingerprint reduction ratio can reach 27%, which greatly reduces the fingerprint data.  
[PERSON] and [PERSON] contributed equally in conceiving the whole proposed codec architecture, designing and performing the experiments, collecting and analyzing the data, and writing the paper. [PERSON] collected the data and implemented the final revision. [PERSON] supervised all aspects of this research. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by the National Natural Science Foundation of China under grant no. 61971015.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Fingerprint Size (per Frame)** \\ \hline Philips & 32 bits \\ Shazam & 32 bits \\ MASK & 22 bits \\ LLE (horizontal) & 18 bits \\ LLE (horizontal + central) & 16 bits \\ \hline \hline \end{tabular}
\end{table}
Table 7: Fingerprint size of different methods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Recall (\%)** & **Precision (\%)** \\ \hline Using DTW & 100 & 99.6 \\ Not using DTW & 99.4 & 98.5 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results of linear speed changes.
## 5 HCV and Microbiota
The majority of these infections arise from the individual's own intestinal microflora. The composition of the intestinal microflora plays a significant role in both physiological and pathophysiological processes within the human gastrointestinal tract; however, the extent of their impact on the liver under various conditions remains ambiguous [36; 37; 38]. While further clinical research is required to validate these approaches, maintaining microbial diversity through dietary or probiotic interventions appears to be a promising avenue for preventing or managing liver disorders linked to HCV. To further elucidate the microbiota-HCV relationship, a comparative analysis of gut microbiomes in HCV-infected patients and healthy individuals was conducted. Stool sample sequencing from six HCV-positive individuals and eight healthy controls revealed marked differences in microbial composition. Notably, alpha diversity was significantly lower in HCV-infected individuals, suggesting a depletion of microbial richness. While Bacteroidetes were found to be more prevalent in HCV patients, healthy individuals exhibited higher relative abundances of Firmicutes, Proteobacteria, and Actinobacteria. Additionally, the probiotic genus Bifidobacterium was exclusively detected in samples obtainedfrom healthy participants, suggesting that beneficial microbial taxa may be diminished in HCV-infected individuals [39]. Despite these insights, the impact of HCV eradication on microbiota composition remains an area of ongoing investigation. A study involving 105 patients with HCV-related cirrhosis sought to determine whether achieving a sustained virological response (SVR) following antiviral therapy influences gut microbiota diversity. Surprisingly, the analysis revealed no statistically significant differences in overall microbiota diversity between individuals who achieved SVR and those who did not (UNIFRAC \(p\) = 0.3) [40]. These findings suggest that while the resolution of HCV infection eliminates viral replication, it does not necessarily restore gut microbiome composition to a pre-infection state. The persistence of microbial dysbiosis post-SVR may be attributed to longstanding hepatic damage, immune alterations, or residual inflammatory processes that persist even after viral clearance. Despite these advancements, research on the relationship between microbiota and HCV remains in its infancy, with a paucity of large-scale studies examining the gut microbiome in a substantial cohort of HCV-infected individuals. Future research should focus on conducting longitudinal analyses to determine how microbiota composition evolves throughout different stages of HCV infection, as well as assessing the potential for microbiota-based therapies to complement existing antiviral treatments.
## 3 Results
### Analysis of the Sequencing Data  
In the present research, conventional analysis and high-throughput sequencing approaches were applied to assess the activity, diversity and composition of both prokaryotic and eukaryotic communities in compost produced at a commercial scale. The results showed microbial development in the compost samples, taken from two different habitats of the composting pile-thermophilic, at 58.1 \({}^{\circ}\)C and mesophilic, at 37 \({}^{\circ}\)C. The diversity of annotated samples can be estimated from the distribution of the species-level annotations in the combined MG-RAST data set.  
The DNA extracted from the two samples taken from the pile was not completely amplified. After merging and quality trimming the raw data, the mesophilic eukaryotes contained 109,291 sequences to tailing 51,675,338 base pairs with an average length of 473 bps. Of the sequences tested, two sequences (0.00%) failed to pass the quality control (QC) pipeline (Table 2). Of the sequences that passed quality control, 107,530 (100%) contained ribosomal RNA genes, and no sequences (0.00%) contained predicted proteins with known functions. When thermophilic eukaryotes were assayed, they contained 3732 sequences to cover 50,707,302 base pairs with an average length of 344 bps. The mesophilic sample showed the content of 148,049 sequences to tailing 48,761,060 base pairs with an average length of 341 bps. After quality filtering, the minimum number of sequences of mesophilic microorganisms was 5518, while for thermophiles it was 3732. The reads of the soil metagenome exhibited an average GC content varying between 48 \(\pm\) 5 and 49 \(\pm\) 6 (Table 2). After alignment, the remaining operational taxonomy units (OTUs) had been clustered at a 3% distance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Mesophilic** & **Therophilic** \\ \hline Total starting base pairs & 48,761,060 & 50,704,302 \\ Base pairs after QC & 1,879,114 & 1,283,854 \\ Average sequence length & 341 \(\pm\) 56 & 344 \(\pm\) 49 \\ GC content, \% & 49 \(\pm\) 6 & 48 \(\pm\) 5 \\ Post QC minimum number of sequence reads & 3732 & 5518 \\ MG-RAST identified rRNA features & 1452 & 1650 \\ MG-RAST identified protein features & 5 & 2 \\ \(\alpha\)—Diversity from MG-RAST annotations, \% & 10 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Metagenome sequences uploaded to MG-RAST for analysis with statistics of the sequence results are shown.  
Figure 3: Microbial activity expressed as released CO2 (**A**) and \(\beta\)-glucosidase activity (**B**) for mesophilic and thermophilic habitats of the composting piles. Results show the mean of 3 replicates \(\pm\) standard error.
# Building Damage Detection from Post-Event Aerial Imagery Using Single Shot Multibox Detector  
[PERSON]  
1 School of Information Science and Technology, North China University of Technology, Beijing 100144, China; [EMAIL_ADDRESS] (W.H.); dh1040760693 [EMAIL_ADDRESS] (H.D.); [PERSON]_ADDRESS] (X.Z.)  
[PERSON]  
1 School of Information Science and Technology, North China University of Technology, Beijing 100144, China; [EMAIL_ADDRESS] (W.H.); dh1040760693 [EMAIL_ADDRESS] (H.D.); [PERSON]_ADDRESS] (X.Z.)  
[PERSON]  
1 School of Information Science and Technology, North China University of Technology, Beijing 100144, China; [EMAIL_ADDRESS] (W.H.); dh1040760693 [EMAIL_ADDRESS] (H.D.); [PERSON]_ADDRESS] (X.Z.)  
1 School of Information Science and Technology, North China University of Technology, Beijing 100144, China; huwei1603@126.  
applied to aerial images are implemented to distinguish objects from the background at a large scale, such as ships at sea [2], aircrafts at airports [3], and cars on highways [4]. Traditional machine vision methods, such as HOG [5], SIFT [6], and Haar [7], can achieve good results in such situations. Given the development of machine learning, researchers have combined traditional machine vision-based methods with shallow machine learning methods, such as support vector machines (SVM) [8]. However, the result of this method is extremely dependent on the effect of feature extraction. In addition, the post-disaster scenario is considerably complex, while key goals are difficult to extract using traditional machine vision methods. Target detection methods-based convolutional neural networks (CNNs), such as R-CNN [9], YOLO [10], and single-shot multibox detector (SSD) [11], have been dominantly used recently to address the aforementioned shortcomings by extracting deep features of images.  
A good training model is primarily required because of the need for many labeled training samples. However, sufficient labeled samples are difficult to obtain immediately after a disaster, thereby resulting in the slow deployment of machine learning target detectors. Therefore, this study solves the problem of extremely few training samples and improves the accuracy of target recognition in post-disaster areas.  
This research proposes the following method. (1) The SSD algorithm training model is used to detect and classify objects in images. (2) The VGG16 [12] convolution auto-encoder is constructed and trained to initialize the weights in SSD using the weight of the training. (3) Image mirroring, rotation, Gaussian noise, and Gaussian blur are applied to expand the training set.  
The remainder of this paper is organized as follows. Section 2 discusses the recent related research. Section 3 describes the study area. Section 4 presents methods for pre-training weights and data expansion. Section 5 compares and discusses the results.
# Reflection Enhancement and Giant Lateral Shift in Defective Photonic Crystals with Graphene
## 2 One-Dimensional Defective PCs  
Figure 1 shows a schematic of the one-dimensional defective PCs. The dielectrics A and B were arranged alternatively on both sides of the defect layer C, forming two Bragg gratings. The defective system could also be viewed as a Fabry-Perot resonant cavity, in which the Bragg gratings are two reflectors and the defect layer plays the role of cavity. A single-layer graphene was embedded in the center of the defect layer. The thickness of dielectrics A and B was \(\lambda_{0}/4n\), where \(\lambda_{0}=1.55\)\(\upmu\)m denotes the midgap (resonant) wavelength of the defective periodic multilayers. The thickness of the defect layer C was \(\lambda_{0}/2n\). The refractive indices of dielectric were \(n_{n}=1.38\) and \(n_{n}=n_{c}=2.35\), respectively.  
Graphene is a two-dimensional material, and we treated it as an equivalent dielectric with a thickness of \(d_{g}=0.34\) nm [29; 30]. The surface conductivity of graphene can be modulated by chemical doping or gate voltages, so the type of graphene can be doped or pure. Subsequently, the relative equivalent permittivity of graphene can be given by \(\varepsilon_{g}=1+\mathrm{i}\sigma_{g}\eta/(kd_{g})\)[28], where \(k=2\pi/\lambda\) is the wave number of incident light in vacuum, \(\eta_{0}\) is the vacuum resistivity, and \(\sigma_{g}\) is the total surface conductivity of graphene. The parameter \(\sigma_{g}\) can be written as \(\sigma_{g}=\sigma^{(1)}+\sigma^{(2)}\left|E_{z}\right|^{2}\), where \(\sigma_{1}\) is the linear surface conductivity of graphene and \(\sigma^{(2)}\) is the nonlinear coefficient of surface conductivity. Here we assumed that the input intensity of light was low enough and ignored the nonlinear effect of graphene. The linear surface conductivity is governed by the Kubo formula [27; 31], which depends on the incident wavelength \(\lambda\), chemical potential \(\mu\), absolute temperature \(T\), and momentum relaxation time \(\tau\). We chose the incident wavelength around \(\lambda_{0}\), and set \(\tau=0.5\) ps, \(T=300\) K in the following study. As a light beam impinges upon the system, the reflection and transmission coefficients are denoted by \(r\) and \(t\), respectively. The two coefficients could be obtained by the transfer matrix method (TMM) [32]. The transmittance \(T\) is identified as \(T=t\)\({}^{*}=l_{1}/l_{1}\), where \(l_{1}\) and \(l_{2}\) are the transmitted and incident intensities, respectively. The reflectance \(R\) is identified as \(R=rr\)\({}^{*}\).
## Data Sources and Availability  
Sentinel-1 is the first of the Copernicus Programme satellite constellations created by the European Space Agency. This space mission is composed of two satellites, Sentinel-1A and Sentinel-1B, carrying a C-band (\(\sim\)5.7 cm wavelength) SAR instrument offering data products in single (HH or VV) or double (HH + VH or VV + VH) polarization [16]. There is a total of 73,128 ground range detected (GRD) images from 2016 to 2018, covering all of mainland China (Figure 1). We determined that the observation frequency at same location ranges from 17 to 128 during 2018, which can meet the monthly water mapping of China. The auxiliary data includes Landsat Operational Land Imager (OLI) image, the SRTM DEM, and GSWE. The water level dataset, GSWE and the time series of inland surface water dataset in China (ISWDC) are used for cross comparison [9; 11; 21]. Table 1 summarizes all of the datasets and tools used in this paper.
# Mind, Machine, and Meaning: Cognitive Ergonomics and Adaptive Interfaces in the Age of Industry 5.0
## 2 Human-Machine Interaction: Definitions and Key Concepts
### Evolution of the Human-Machine Interaction
#### 2.1.3 Industrial Examples and Use Cases  
The application of advanced HMI systems is widespread across various industrial domains. In smart manufacturing, collaborative robots (cobots) work side by side with human operators using sensor-based and vision-guided systems to adjust actions in real time. These robots are commonly deployed in assembly lines, reducing physical strain and improving precision [8].  
In the automotive industry, AR-based interfaces guide workers through maintenance procedures, while wearable exoskeletons support ergonomically challenging tasks. Digital twins are used to simulate production scenarios, enabling predictive maintenance and operational optimization. Moreover, wearable sensors and AI-driven analytics track physiological indicators of fatigue, workload, and posture, creating closed-loop systems that protect worker health and enhance performance [9; 11].  
Recent studies have also highlighted the importance of maintaining the \"sense of agency\" (SoA) in HMI systems. SoA--the subjective experience of initiating and controlling one's actions--has become critical in systems where humans supervise or intermittently interact with autonomous technologies. Maintaining SoA ensures that users remain cognitively engaged, which is essential for safety and accountability in applications like automated driving and remote robotic surgery [12].
# Comparison and Combination of Mobile and Terrestrial Laser Scanning for Natural Forest Inventories
## 3 Datasets
### MLS and TLS Data Acquisition  
The MLS data was recorded on two dates (March 2017 under leaf-off conditions; August 2017 under leaf-on conditions) using a Riegl VMX-250 Mobile Mapping System (Figure 1). The Mobile Mapping System consists of two Riegl VQ-250 laser scanners as well as an integrated inertial measurement unit (IMU) and global navigation satellite system (GNSS) equipment and six digital cameras. Forest corridors in the study area were captured twice in opposite driving direction (Figure 2). Overall the MLS trajectory had a length of 20.0 km (forest roads in both directions), and data was captured up to 100 m into the forest on each side of the forest road. We recorded the MLS point cloud at a scan rate of 300 kHz (per scanner) at a driving speed of 10-15 km/h. The coverage of the surveyed area thus amounts to about 150 ha. TLS data was recorded at the same time at two plots using a Riegl VZ-400i. We sampled circular 500 m\({}^{2}\) plots (radius of 12.62 m each). Each plot was scanned from five scanner positions in a multiple scan mode, one in the center of the plots and the other four positions spread in the four cardinal directions with a distance of 15 m from the center, with an angular resolution of 0.05 deg or 0.04 deg, respectively. At the center position, the scanner was tilted by 90\({}^{\circ}\) to overcome the limitation of the panoramic field of view. Outside the plot radius, the scanner was tilted by 30\({}^{\circ}\). The instrument height was 1.30 m.  
Figure 2: **(a,b)** Study area with the MLS trajectory (green line) and the two sample plots (red circles). **(c)** Vertical slice through MLS point cloud under leaf-off or (**d**) leaf-on conditions with a 35 m (yellow) and 100 m (blue) corridor from the trajectory.
# Bearing Fault Diagnosis Based on Small Sample Learning of Maml-Triplet
## 3 Construction of the Dataset and Model
### Construction Method of the Maml Neural Network Dataset and Triplet Neural Network Data  
As shown in Tables 1 and 2, the dataset for training the Maml neural network is constructed using Group A signals. The data used by the maml neural network are no longer a single signal, but the fault classification task is used as the input data. The \(n\)-way \(k\)-shot model is taken as an example, that is, \(n\) fault types and \(k\) signal data in each type as an example. Firstly, \(n\) fault types are randomly selected from nine signal types of Group A signal. According to the above data preprocessing method, \(k\) signals are randomly intercepted from a fault type. After the order is disturbed, an \(n\)-way \(k\)-shot fault classification task is generated. The above operations are repeated to generate multiple fault classification tasks and to form a dataset of the Maml neural network.  
A dataset is built for training the triplet neural network based on Group B signals. At each input signal, three signals are simultaneously selected as a triplet, which are positive examples, anchor examples, and negative examples. In the training process, after each weight update, the eigenvectors of the three signals are calculated respectively according to the updated Triplet neural network, and the difficult triplets are selected according to the Euclidean distance between the characteristic signals. Note that the triplet neural network dataset is not created at the beginning of the training, but is updated and selected to form a new dataset during the network training.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Bearing** \\ **Position** \\ \end{tabular} } & \multicolumn{4}{c}{**Fault Frequencies (Multiple of Shaft Speed)**} \\ \cline{2-5}  & **BPFI** & **BPFO** & **FTF** & **BSF** \\ \hline Motor end & 5.415 & 3.585 & 0.3983 & 2.357 \\ Fan end & 4.947 & 3.053 & 0.3816 & 1.994 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Bearing fault frequencies. BPFI: ball pass frequency, inner race; BPFO: ball pass frequency, outer race; FTF: fundamental train frequency (cage speed); BSF: ball (roller) spin frequency.
# INSPIRE-SAT 7, a Second CubeSat to Measure the Earth's Energy Budget and to Probe the Ionosphere
## 2 Scientific Objectives of the INSPIRE-SAT 7 Mission
### Total Solar Irradiance and Solar Spectral Irradiance (SSI) in the Herzberg Continuum  
The influence of the Sun on climate and the role of solar variability in climate change remains a topic of considerable scientific and societal importance. TSI [12] provides nearly all the energy driving the Earth's climate system and represents a key scientific parameter. The solar spectrum [13] and its variability also represent key inputs not only for solar physics but also for climate physics. Since the variation of UV solar irradiance during a Solar Cycle is much larger, in proportion, than variations in total solar irradiance, it plays an important role in regional climate change through interactions with stratospheric ozone. UV solar irradiance and its variability thus represents a key input for both climate and solar physics. New technologies to carry out solar measurements will be tested in orbit.