esa-sceva/satcom-chunk-collection huggingface dataset:
# Electrospun Nanofibrous Membranes for Tissue Engineering and Cell Growth
## 1 Introduction  
The cultivation of cells in T-flasks is a common method in biotechnology. For the classical cultivation of adherent cells in roller bottles or cell culture pellets, manufacturing costs are very high [1]. Microcarriers and hollow fibers are an alternative to the classical systems and can replace these bioreactor systems [2, 3]. Such hollow fibers can be prepared from various polymers [4, 5].  
Today's single-use fixed-bed bioreactors provide consistent product quality from batch to batch, which is vital in pharmaceutical processes. With continuous bioprocesses, smaller production volumes and higher product concentrations are possible. Such ready-to-use bioreactors are packed with non-woven macroporous carriers or rolled membranes. The fabric often consists of polyethylene terephthalate (PET) microfishers, which are surface treated to make them hydrophilic and biocompatible for cell attachment [6, 7, 8].  
There are, however, approaches to support cell growth more effectively, e.g., by using nanofiber mats as substrates for cell growth [9]. Such new nanostructured substrates can improve cell growth in tissue engineering and biotechnology by guiding cell growth and even improving stem cell differentiation. On the other hand, tissue engineering often necessitates 3D substrates, which cannot be reached by bioreactors or T-flasks, while nanofiber mats can be draped according to the requirements of an experiment.  
Such nanofiber mats can be produced by electrospinningning from various polymers and polymer blends, as well as polymers with nanoparticles included [10, 11, 12]. With electrospinning techniques being developed further, nanofiber mats have been increasingly used for biotechnological applications, including cell cultivation [13, 14, 15, 16].  
Besides the large specific surface of such nanofiber mats, specific electrospinning techniques or substrate preparations enable spinning oriented fibers, which is not onlysupportive in terms of mechanical properties, but also for growing cells on these substrates in a defined orientation and in many cases with a higher proliferation rate [17; 18; 19; 20].  
Here, we give a brief overview of recent electrospinningning techniques, followed by reviewing materials typically used for the preparation of electrospun nanofiber mats as scaffolds for mammalian cell growth. The focus is put on Chinese hamster ovary (CHO) cells, which are often used for experiments in such cell cultures, not only for the production of therapeutic proteins, but also as model cells for biotechnological studies [21; 22; 23].
# Application of Electrical Network Frequency of Digital Recordings for Location-Stamp Verification
###### Abstract  
Electrical network frequency (ENF) is a signature of a power distribution grid. It represents the deviation from the nominal frequency (50 or 60 Hz) of a power system network. The variations in ENF sequences within a grid are subject to load fluctuations within that particular grid. These ENF variations are inherently located in a multimedia signal, which is recorded close to the grid or directly from the mains power line. Thus, the specific location of a recording can be identified by analyzing the ENF sequences of the multimedia signal in absence of the concurrent power signal. In this article, a novel approach to location-stamp authentication based on ENF sequences of digital recordings is presented. ENF patterns are extracted from a number of power and audio signals recorded in different grid locations across the world. The extracted ENF signals are decomposed into low outliers and high outliers frequency segments and potential feature vectors are determined for these ENF segments by statistical and signal processing analysis. Then, a multi-class support vector machine (SVM) classification model is developed to verify the location-stamp information of the recordings. The performance evaluations corroborate the efficacy of the proposed framework.  
digital recordings; electrical network frequency (ENF); feature vectors; location-stamp; root MUSIC; SVM classifier Article
# Two Novel Multidimensional Data Analysis Approaches Using InSAR Products for Landslide Prone Areas
## 2 Study Area and Use Data Sets
### Sentinel-I SAR Data Set and Specifications  
Sentinel-1 is a pair of C-Band radar satellites (Sentinel-1A and Sentinel-1B) operated by the European Space Agency (ESA). Thanks to SAR (synthetic aperture radar) technology, the satellite can collect data day and night and in all weather conditions. This ensures that cloud cover or lack of light does not degrade data quality. Sentinel-1's C-band sensor operates at 5.405 GHz, and provides data in both co-polarization and cross-polarization modes [27]. This enables detailed analyses of elements, such as ground moisture, vegetation, and water surface, and interferometric applications, especially ground deformations, can be measured at the millimeter scale [28; 29].  
The utilization of the satellite pair (Sentinel-1A and Sentinel-1B) enables the revisit of the same target areas at an average interval of six days. It is evident that the frequency of revisit time offers substantial benefits in the context of monitoring dynamic processes and enhancing temporal accuracy [30].  
Sentinel-1 can collect data in different imaging modes, the most widely used of which is the Interferometric Wide Swath (IW) mode. The TOPSAR IW mode divides the radar beam into three sub-swaths (IW1, IW2, and IW3), allowing for large areas to be scanned with high phase sensitivity. This provides the detailed phase information required for the construction of interferograms. In this way, the separation of topographic effects and the correction of atmospheric delays in interferometric analyses can provide more accurate results [31]. However, the IW mode allows for combining a large swath width (250 km) with a moderate geometric resolution (5 m by 20 m) [32].  
A total of 431 Single Look Complex (SLC) images acquired between 2015 and 2020 from the Sentinel-1 satellite were obtained via the LiCSAR platform, developed by COMET (Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics). Table 1 presents the characteristics of the data obtained through this platform, and the parameters related to the SBAS processing steps. These images underwent preprocessing steps including thermal noise removal, radiometric calibration, orbit correction, and geometric alignment. This preprocessing phase is critical for increasing the accuracy and precision of the interferograms [33].  
Figure 2 presents the perpendicular baselines of the interferograms derived from both ascending (a) and descending (b) orbits. The interferograms generated for multi-temporal InSAR analysis were evaluated based on both temporal and geometric criteria. Interferograms with coherence values below 0.05 are highlighted in red, and were excluded from the analysis. This filtering was carried out to enhance the reliability of the results.  
Figure 1: The lithological configuration of the study area is adapted from the 1:100,000 scale geological maps published by MTA, with modifications [23].
## 2 Materials and Methods
### Improved-YOLOv5  
#### 2.2.1 SimSPPF (Simplified SPPF)  
The SPPF (Spatial Pyramid Pooling with Factorization) module in YOLOv5 is essential for capturing context information at multiple scales by pooling features with different window sizes. The CBS (Conv-BN-SiLU) structure is used by the original SPPF module in YOLOv5, which is a convolution layer followed by batch normalization and the Sigmoid Linear Unit (SiLU) function. SimSPPF (Simplified SPPF) was proposed in YOLOv6 [32], an upgraded version of the SPPF that uses the CBR (Conv-BN-ReLU) structure in the SPP block, where the convolution layer is followed by batch normalization and ReLU activation. When deciding between YOLOv5 and YOLOv6, the choice should be made based on priorities and constraints. If the primary concern is accuracy, YOLOv5 with the original SPPF module is the better option as it captures context information at multiple scales effectively using the CBS structure with the SiLU activation function. If computational efficiency is the top priority, then YOLOv6 with the SimSPPF module is preferable as it uses the CBR structure with the ReLU activation function, which is less computationally expensive. While SimSPPF has slightly lower accuracy, it might be an acceptable tradeoff for our application. The goal of this modification was to reduce the computational complexity of the SPP block while maintaining the accuracy. The main distinction between the SPPF and the SimSPPF is the activation function used after the batch normalization layer in the SPP block's convolution layer. The SimSPPF employs the ReLU activation function, whereas the SPPF employs the SiLU activation function. The authors discovered that heavily implementing the SimSPPF only resulted in a small increase in accuracy with increased computational complexity, while also improving the FPS. Figure 2 depicts the structure of the SimSPPF Module.  
The SimSPPF module was used in our model in this research project. The decision to use the SimSPPF module was based on the observed advantages of SimSPPF over SPPF, such as the reduced computational complexity and higher FPS while maintaining good object detection accuracy. The equations are shown in Equations (1)-(5) [33].  
\[F1=CBR(F) \tag{1}\]  
\[F2=\text{ Maxpooling }(F1) \tag{2}\]  
\[F3=\text{ Maxpooling }(F2) \tag{3}\]  
\[F4=\text{ Maxpooling }(F3) \tag{4}\]
# Remote Sensing Technologies for Monitoring Argane Forest Stands: A Comprehensive Review
## 5 Challenges and Limitations  
While remote sensing offers remarkable capabilities for monitoring _Argania spinosa_, it also faces several challenges and limitations. In arid regions where _Argania spinosa_ thrives, cloud cover can impede satellite imagery acquisition, limiting data availability. Additionally, the need for ground truth data to validate remote sensing results can be challenging, especially in remote areas with dense tree cover that can hinder GPS accuracy and accessibility to specific points. In such environments, satellite signals can be obstructed or weakened by foliage, causing inaccuracies in location determination, while navigating dense vegetation to reach data collection points can be labor-intensive, impacting fieldwork efficiency and data reliability. Data accuracy may also vary due to factors such as sensor calibration and atmospheric interference. The spatial resolution of satellite images can indeed vary, and lower-resolution images may not be sufficient for detailed image classification, especially in heterogeneous landscapes where high-resolution data are crucial for distinguishing small-scale features and ensuring accurate monitoring. Furthermore, image distortions in uneven terrain can indeed affect the quality and accuracy of remote sensing data. Terrain variations can cause geometric distortions and shadowing effects, which can complicate image analysis and interpretation. Additionally, the digitization footprint associated with different sensing techniques, including the storage, processing, and computational demands, can present significant challenges. The interpretation of remote sensing data requires expertise, and there can be limitations in distinguishing between stress factors, like drought and disease, solely through spectral signatures. Moreover, the cost of acquiring and processing remote sensing data can be a barrier for some research and conservation efforts. Despite these challenges, ongoing advancements in technology and collaboration among researchers offer promising avenues to address these limitations and further enhance the effectiveness of remote sensing in monitoring _Argania spinosa_ health, productivity, and response to drought stress.
## 5 Conclusions  
In this study, reservoir optimization was investigated based on climate change and the shark algorithm. A base period (1980-1999) and future period (2046-2065) were considered. The scenario A\({}_{1}\)B and the HAD-CM3 model were used to predict maximum and minimum temperatures and precipitation. Results showed that the maximum and minimum temperatures increased. Additionally, total precipitation in the future period was less than that in the base period. Runoff volume was then simulated based on the IHACRES model. Results showed that the runoff volume increased in the future period, and, as a result, inflow into the reservoir increased. Additionally, the results showed that irrigation demand, based on computed climate variables in the future period, was higher than that in the base period, and the amount of water released in the future period was less than that in the base period. The different indices, such as reliability and vulnerability, indicated that severe deficits are projected to occur in the future period. Furthermore, the shark algorithm exhibited considerable potential for effectively solving complex problems. In future studies, hybrid methods, such as a fuzzy shark algorithm, can be utilized and evaluated. One of the major drawbacks of the current study is that the proposed mechanism and procedure has been applied and examined using a relatively obsolete climate change scenario, and, hence, the achieved results could change while utilizing the most recent scenario. Therefore, there is a need to apply this mechanism and procedure using the most updated climate change scenario to update the operation rule.  
Formal analysis, M.E., A.H.E.-S., S.K., H.K. and S.F.; Methodology, M.E., H.K. and S.-F.M.; Supervision, A.E.-S.; Validation, F.O.; Writing--original draft, L.S.H., F.O., S.F., A.N.A., M.S.H., N.S.M. and H.A.A.; Writing--review & editing, M.H.B.Z.  
This research financially supported from Bold 2025 (grant coded RJO 10436494) by Innovation & Research Management Center (iRMC), Universiti Tenaga Nacional and from research grant coded UMRG RP025A-18 SUS funded by the University of Malaya and by Ministry of Higher Education Malaysia from Fundamental Research Grant Scheme (FRGS, Grant No: FRGS/1/2019/TK01/UNITEN/02/3).  
The authors appreciate so much the facilities support by the Civil Engineering Department, Faculty of Engineering, University of Malaya, Malaysia and Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Iran.  
The authors declare no conflict of interest.  
Figure 17: Irrigation demands based on the RCP 8.5 scenario.
# A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors
## 3 Methodology and Application of Indoor Localization in Various Fields
### Indoor Localization Applications and Methodologies in Healthcare
#### 3.1.4 Hybrid Systems for Critical Situations  
[PERSON] et al. [58] proposed a hybrid indoor localization system architecture that combines multiple techniques, which is shown in Figure 13, to improve accuracy, robustness, and adaptability in various environments. The system operates in two distinct modes: an infrastructure-dependent mode and an infrastructure-independent mode. The infrastructure-dependent mode relies on WLAN signals and smartphone sensors for accurate positioning, leveraging Wi-Fi access points as reference points to correct drift in Pedestrian Dead Reckoning (PDR). In cases where Wi-Fi signals are unavailable, the system switches to the infrastructure-independent mode, using only smartphone sensors to track movement through PDR. This ensures continuous localization even in environments where Wi-Fi infrastructure is limited or disrupted [58].  
The system uses a smartphone-based sensor collector equipped with an accelerometer, magnetometer, gyroscope, and Wi-Fi scanner to collect movement and environmental data. The localization process begins with step detection and step length estimation, facilitated by the accelerometer. To reduce noise and random errors, the system applies low-pass filters and mean filters to the sensor data. For determining the user's orientation, a Quaternion-based Extended Kalman Filter (EKF) is used, which enhances heading accuracy by fusing data from the magnetometer and gyroscope [58].  
When Wi-Fi signals are available, the system enhances positioning accuracy using trilateration based on Wi-Fi access points. The system intelligently fuses data from PDR  
Figure 12: Positioning System Classification for IoT-Based Healthcare Applications.  
Figure 13: System architecture of the hybrid model.  
and WLAN fingerprinting to maintain accurate positioning, even in dynamic or partially obstructed environments. The real-time position is continuously updated, ensuring a low error rate and reliable localization. This hybrid approach allows the system to adapt to varying conditions, making it highly suitable for applications such as emergency evacuations, healthcare environments, and other critical indoor scenarios. By seamlessly switching between localization modes based on Wi-Fi availability, the system achieves consistent performance, scalability, and robustness, ensuring reliability in diverse and challenging environments [58].
# Sub-Band Backdoor Attack in Remote Sensing Imagery
## 2 Related Work
### Backdoor Attacks and Defense Mechanisms  
A backdoor attack is a type of model poisoning in which the attacker embeds a trigger in an image and labels it with a chosen target label. This poisoned data is then used to train the model. After training, the backdoored model performs well on clean datasets but misclassifies any image with the embedded trigger as belonging to the target class, regardless of the actual content in the image. The attacker can either distribute the poisoned model to victims or release the poisoned dataset for them to use. BadNets [11], one of the initial types of backdoor attacks, involves the attacker poisoning a portion of the training data with a specific trigger before sending it to the victim. This trigger could be a simple pattern in the input image. Once the victim uses these poisoned datasets for training, the model becomes compromised.  
At the same time, researchers were developing potential defense approaches. Neural cleanse [12] provides novel algorithms for detecting and identifying the backdoor in a poisoned model and reverse-engineering the hidden trigger. The Neural cleanse approach shows effectiveness in various benchmark datasets, such as GTSRB [39], MNIST [40], CIFAR-10 [41], and SVHN [42], against popular backdoor attack techniques. Over time, researchers have developed Trojan backdoor attacks [43], reflection attacks [44], and latent vector attacks [45]. Similarly, defense methods have also been developed, including TABOR [13], DeepInspect [18], Gangsweep [16], etc.
## 3 Proposed Method
### Scale Estimation  
The translation estimation tracks the horizontal and vertical movements of objects. Thus, tracking only the translation of the object has limited performance object tracking. The DSST [22] proposed scale space for accurate scale estimation. Scale space expresses the data in 3 dimensions; the size is \(P^{m}\times P^{n}\times D\). Here, \(P^{m}\), \(P^{n}\) and \(D\) are the height, width and dimension, respectively. We compose the image pyramid for scale estimation with \(\hat{W}(x_{c},y_{c})\). The equation for the composition of the image pyramid consisting of various sizes is as follows.  
\[\eta_{l}=s^{c},\ c\in\left\{\left\lfloor\frac{D}{2}\right\rfloor-1\,\ \cdots\,\ \left\lfloor\frac{D}{2}\right\rfloor-D\right\} \tag{14}\]  
The scale factors \(\eta_{l}\) contains large values to small values to compose the image pyramid. \(s\) is the factor for the scale step. The dimension of features is defined by \(l\in\{1,\ldots,D\}\). After the image pyramid has been composed, we can calculate the scale response using the equation below [22].  
\[\beta=F^{-1}\left(\frac{\sum\limits_{l=1}^{D}F(A_{l})\cdot F(Z_{l})}{F(S)+ \lambda^{\prime}}\right) \tag{15}\]  
where \(Z_{l}\) means the object, \(A_{l}\) is the desired output and \(S\) is the kernel correlation result. The scale response \(\beta\) has \(D\)-th values, and the index of highest value means scale factor \(\eta^{*}\).  
\[\eta^{*}=\underset{l\in\{1,\ -\,\ D\}}{\arg\max}\left(\max(\beta_{l})\right) \tag{16}\]  
The picked scale factor \(\eta^{*}\) is multiplied by the size and center position of all the blocks.
## 3 Results
### Expansion Efficacy  
Table 2 exhibits within-group comparisons of the width changes between T1 (baseline) and T3 (expansion achieved) in each crossbite group. The percentage of the initial width increased by treatment was calculated, too, and expressed as T3-T1(%). The changes were statistically significant (\(p\)\(\leq\) 0.05) for all measurements in all three groups, except for the interacinine width in the unilateral crossbite group.  
The largest expansion was achieved at the second premolar level in all three groups, being 4.86 mm in the bilateral crossbite group, with T3-T1(%) of 11.70%; 3.41 mm in the single-tooth crossbite group, with T3-T1(%) of 8.01%; and 2.54 mm in the unilateral crossbite group, with T3-T1(%) of 5.75%.  
Expansion was greater at the first molar versus first premolar level in both the unilateral (2.08 mm vs. 1.65 mm) and bilateral (3.95 mm vs. 3.38 mm) crossbite groups, whereas it was greater at the first premolar versus first molar level (3.02 mm vs. 2.67 mm) in the single-tooth crossbite group.  
Table 3 displays between-group comparisons of the width changes (T1-T3). The groups did not significantly differ in interacinine width changes (\(p\) = 0.142); however, significant between-group differences were observed in the amount of expansion at the first premolar level (\(p\) = 0.03), second premolar level (\(p\) = 0.026), and molar level (\(p\) = 0.034). At the first premolar level, the mean expansion was significantly larger in the bilateral versus unilateral crossbite group (mean difference of 1.73 mm; 95%CI 0.06-3.39 mm), whereas there was no significant difference between the single-tooth and unilateral or bilateral crossbite groups.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Group** \\ **(Type of Crossbite)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Number of Patients** \\ **(Male/Female)** \\ \end{tabular} } & \begin{tabular}{c} **Total Mean Number** \\ **of Aligners (SD),** \\ **Including** \\ **Refinements** \\ \end{tabular} & \
\begin{tabular}{c} **Mean Treatment Time** \\ **for the First Set of** \\ **Aligners in Months** \\ **(SD)** \\ \end{tabular} &
\begin{tabular}{c} **\% of Patients that** \\ **Used Intermaximillary** \\ **Elastics** \\ \end{tabular} \\ \hline Unilateral & 15 (7/8) & 28 (7.05) & 15.5 (4.88) & 73\% \\ \hline Bilateral & 15 (6/9) & 32 (15.57) & 15.2 (7.54) & 66.67\% \\ \hline Single-Tooth & 16 (7/9) & 31 (10.23) & 14 (3.58) & 46.67\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the sample. SD: standard deviation, %: percentage.  
At the second premolar and first molar levels, the expansion was also significantly larger in the bilateral versus unilateral crossbite group at the second molar (mean difference of 2.31 mm; 95%CI 0.25-4.37 mm) and first molar (mean difference of 1.87 mm; 95%CI 0.11-3.63 mm) levels.