esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Motion Dynamics Input Search Algorithm
### Determining Search Directions Based on the Received User Input
These sensors require calibration, synchronization, and integration with the existing control and processing framework. This can be challenging and may necessitate significant changes to the hardware and software architecture. In scenarios where user inputs are assisted by systems that modify them, such as stabilization systems, the input data may not directly map to the proper movement code. For instance, if a stabilization system smoothens abrupt joystick movements to prevent sudden jerks, the input data might be mapped incorrectly, because incorrect data would be used in movement code calculation. The MDIS algorithm should match the stabilization mechanism's adjustments and use these modified input data when calculating movement codes, as it more accurately represents the actual control inputs being applied to the drone. Another important factor to consider is that our primary interest lies in the direction of acceleration, not just the drone's movement. This is due to the fact that MVs are refined based on a starting MV. For instance, if the drone is moving forward but begins to decelerate, the backward motion model should be employed, despite the drone's continued forward motion. The same goes with all other movement types, not just the forward/backward motion. Very small accelerations and decelerations are not problematic, because the fractional pixel search, which is unchanged in MDIS, can handle small changes in MVs. Specifically, this last case of small changes in acceleration refers to cases in which the user's fingers aren't completely steady, so the joystick push intensities can slightly vary from frame to frame. The MDIS version tested in this article determined the final movement codes by following the following described principles:  
* Consecutive frames condition--A joystick had to maintain the highest push intensity for several consecutive frames (eight in the tested MDIS version). Only after this consistency was maintained over several frames did the corresponding movement code start being written to the final direction output file. * Acceleration threshold condition--To ensure we are capturing acceleration, the stick push intensity in the current frame had to differ from the previous frame by a specific threshold (e.g., 0.05). If any of these conditions were not met, a '0' was written to the final directions output file. In the current MDIS implementation, only the nine search modes are implemented. The combinations of multiple models, such as forward and downward motion, explained earlier, are not yet implemented. Whenever it is unclear which search model should be used, a '0' is written in the textual movement file, which means a normal DS is used for that frame. This is an improvement that can be implemented in future research.
# Influence of Carbon: Metal Ratio on Tribological Behavior of Mo-W-C Coating
## 3 Results
### Friction Behavior during lubricated Sliding
It was observed that a high C/(Mo+W) ratio retained the graphitic nature of C2 and formed lubricious WS\({}_{2}\) during sliding, whereas low C/(Mo+W) ratios converted both C4 and C6 from their as-deposited condition into a more graphitic state during sliding and simultaneously promoted the WS\({}_{2}\) formation, leading to a very low \(\mu\). Figure 6: Friction behavior of C2, C4 and C6 in lubricated condition at ambient temperature (Cr/C coating is used as a reference). Figure 8 shows the friction behavior of C2, C4 and C6 in lubricated conditions at 200 \({}^{\circ}\)C. C2 showed a \(\mu\)-0.074 during initial \(\sim\)270 m run-in distance, followed by a drastic decrease to \(\mu\)-0.036 for the rest of the steady-state period. C4 showed a \(\mu\)-0.095 up to \(\sim\)440 m run-in period, and then a gradual decrease in the friction curve led to \(\mu\)-0.065. A similar trend was observed for C6, which showed \(\mu\)-0.087 during initial \(\sim\)630 m run-in distance, followed by a slight decrease to \(\mu\)-0.061 for rest of the gradually decreased friction curve. It is important to note that, after the initial run-in period, C2 showed a very stable friction behavior, whereas a gradually decreasing trend was observed in both friction curves of C4 and C6. This indicated a possibility for C4 and C6 providing further  
Figure 7: Raman spectra collected from C2, C4 and C6 (**a–c**) in as-deposited condition and (**d–f**) within the wear tracks after lubricated sliding at ambient temperature. reduction in \(\mu\) as compared to C2 if the sliding tests were continued for significantly large distances. This difference in friction behavior led to an overall \(\mu\) of \(\sim\)0.042, \(\sim\)0.072 and \(\sim\)0.07 for C2, C4 and C6, respectively, during lubricated sliding at 200 \({}^{\circ}\)C. In contrast to ambient-temperature tests, the formed tribolayer at 200 \({}^{\circ}\)C was found to be strongly adhered to the steel counterparts than the wear track of the coatings.
###### Abstract  
The targets in Synthetic Aperture Radar (SAR) images are often tiny, irregular, and difficult to detect against complex backgrounds, leading to a high probability of missed or incorrect detections by object detection algorithms. To address this issue and improve the recall rate, we introduce an improved version of YOLOv8 (You Only Look Once), named MDD-YOLOv8. This model is not only fast but also highly accurate, with fewer instances of missed or incorrect detections. Our proposed model outperforms the baseline YOLOv8 in SAR image detection by utilizing dynamic convolution to replace static convolution (DynamicConv) and incorporating a deformable large kernel attention mechanism (DLKA). Additionally, we modify the structure of the FPN-PAN and introduce an extra detection header to better detect tiny objects. Experiments on the MSAR-1.0 dataset demonstrate that MDD-YOLOv8 achieves 87.7% precision, 76.1% recall, 78.9% mAP@50, and 0.81 F1 score. These metrics show an improvement of 8.1%, 6.0%, 6.9%, and 0.07, respectively, compared to the original YOLOv8. Although, MDD-YOLOv8 increases parameters by about 20% and GFLOPs by 53% more than YOLOv8n. To further validate the model's effectiveness, we conducted generalization experiments on four additional SAR image datasets, proving that MDD-YOLOv8's performance is robust and universally applicable. In summary, MDD-YOLOv8 is a robust, generalized model with strong potential for industrial applications.  
dynamic convolution; deformable large kernel attention; YOLOv8; synthetic aperture radar; deep learning Article
## 2 Materials and Methods
### Satellite Imagery Selection and Processing
#### 2.5.2 Sentinel-1 and Sentinel-2 Image Pre-Processing Methods  
Sentinel-2 multispectral bands with primary resolutions of 20 m \(\times\) 20 m pixels were resampled to 10 m to match the remaining high-resolution bands. To achieve this, we applied the nearest neighbour resampling method within the Sentinel Applications Platform (SNAP v. 8.0.8, Snap Inc., Santa Monica, CA, USA). Then, all the pre-processed bands were converted to true reflectance by dividing the digital numbers by the quantification number (10,000).  
Sentinel-1 image pre-processing followed the workflow published in [66], which consisted of seven processing steps implemented through SNAP. The speckle noise reduction was conducted through the application of a Lee-Sigma filter [67]. Range Doppler terrain correction was made using the DEM produced in this study (for more details, consult [66; 67; 68]).
## 3 Motivation  
Common SQLI black-box detection methods, which rely on predefined rules and patterns, are highly dependent on the quality of predefined rules. Unfortunately, they often fail to encompass all potential attack scenarios, exhibiting a lack of flexibility and poor adaptability to novel or mutated SQLI. Additionally, the rigidity of these rules can lead to false positives, where legitimate queries are mistakenly identified as attacks, or false negatives, where actual attacks are not detected. This results in low detection accuracy and efficiency.  
In contrast, AI-based SQLI black-box detection methods leverage a large corpus of training data to learn potential attack patterns, thus eliminating the dependence on comprehensive and high-quality predefined rules. However, these methods face significant challenges due to the need for more high-quality training sets and the complexities involved in accurate problem modeling. Insufficient training sets can result in under-trained models that fail to effectively recognize real-world attack patterns, while inadequate SQLI modeling might prevent the models from comprehensively and effectively capturing all types of attack behaviors. This can severely impact the detection system's accuracy and increase the likelihood of false positives and false negatives. Consequently, AI-based SQLI black-box detection methods often need better portability.  
LLMs have developed an extensive knowledge base by training on vast amounts of text data, enabling them to provide insightful and accurate information across a wide array of topics and domains. The emergence of LLMs reduces the dependence of AI-based approaches on high-quality datasets. By pre-training on large and diverse datasets, these models can understand complex texts and lessen the need for finely labeled data. In SQLI black-box detection, LLMs can utilize their rich linguistic understanding to identify better and understand various patterns of SQLI. Additionally, their a priori knowledge can improve detection accuracy even if the training data are insufficient or of low quality. The capability of LLMs to process and understand long-form text, along with their strong contextual comprehension and reasoning skills, allows them to excel in complex modeling and inference, delivering continuous, relevant, and meaningful judgments. Moreover, LLMs can be fine-tuned with a small set of examples to rapidly adapt to various downstream tasks, and their robust transfer learning ability ensures adaptability, providing a robust and reliable solution for SQLI black-box detection.  
Despite the promising potential of LLMs in SQLI black-box detection, current research has not deeply explored their application in this domain. To address this gap, we designed an exploratory experiment to further investigate the capabilities of LLMs in SQLI black-box detection. Based on the conclusions drawn from the exploratory experiment, we developed SqliGPT, an LLM-powered SQLI black-box scanner.
# On-Demand Maintenance Method Using Fault Prediction to Reduce Elevator Entrapment
## 1 Introduction
(3) According to the risk index checklist and historical root cause analysis, an on-demand maintenance plan was composed and examined in City H through field experiments. The results demonstrate that this solution achieves dual optimization compared with the scheduled method: entrapment incidents were reduced, and the maintenance workload decreased from 382 units to 54 units, representing an 85.86% reduction. The subsequent sections of the paper are structured in the following manner: Section 2 provides a brief overview of the historical fault cause analysis; Section 3 introduces the data and models for entrapment fault prediction and the design of an entrapment risk index checklist; Section 4 presents the design and implementation of the on-demand maintenance plan; Section 5 compares and analyzes the results between the applied and the scheduled maintenance methods; and Section 6 presents the conclusion.
# Effects of Different Variables on the Formation of Mesopores in Y Zeolite by the Action of CTA+
## 2 Materials and Methods
### Characterization  
X-ray diffractograms were acquired using a Rigaku MiniFlex 600 diffractometer (Tokyo, Japan) operated with Cu K\(\alpha\) radiation (\(\lambda\) = 1.5418 A). The relative crystallinity (RC) was determined using the ratio of the sums of the peak areas at 23.3\({}^{\circ}\), 26.6\({}^{\circ}\), and 30.9\({}^{\circ}\) 20 for the modified samples, relative to the original sample, as recommended by ASTM. The original zeolite was considered as a standard, with an assumed crystallinity of 100%.  
The textural properties of the zeolites were evaluated using nitrogen physisorption isotherms acquired using a Micromerities ASAP 2020 system. The mesopore diameter distribution was determined by the Barret-Joiner-Halenda (BJH) method, employing the desorption branch of the  
\begin{table}
\begin{tabular}{c c c} \hline
**Sample** & **Base Molar Ratio (x)** & **Surfactant Molar Ratio (y)** \\ \hline YB0-S0.1 & 0 & 0.1 \\ YB0.02-S0.1 & 0.02 & 0.1 \\ YB0.04-S0.1 & 0.04 & 0.1 \\ YB0.06-S0.1 & 0.06 & 0.1 \\ YB0.08-S0.1 & 0.08 & 0.1 \\ YB0.08-S0 & 0.08 & 0 \\ YB0.08-S0.02 & 0.08 & 0.02 \\ YB0.08-S0.04 & 0.08 & 0.04 \\ YB0.08-S0.06 & 0.08 & 0.06 \\ YB0.08-S0.08 & 0.08 & 0.08 \\ \hline \end{tabular}
\end{table}
Table 1: Modification conditions used for preparation of the materials.  
isotherm. The micropore volume was determined by the t-plot method, using a 0.3-0.5 nm thickness interval. The mesopore volume was determined by the NLDFT method for pores with cylindrical geometry based on the functional density [10; 15]. The total quantities of acid sites in the materials were determined by the Ammonia Temperature Programmed Desorption technique (TPD-NH\({}_{3}\)), employing a Micromeritics AutoChem II 2920 chemisorption analyzer.  
The global silicon/aluminum ratios were determined by energy dispersive spectroscopy (EDS). The analyses were performed using a field emission gun (FEG) electron microscope operated at 20 kV with the samples dispersed on double-sided adhesive carbon tapes.  
Quantification of silicon in the filtrate was performed by chemical analysis using an Optima 8000 Inductively Coupled Plasma Optical Emission Spectrometer (ICP-OES).  
Analysis of the zeolites by \({}^{27}\)Al nuclear magnetic resonance (NMR) was performed using a Bruker Avance III-400 system operated with a 9.4 T magnetic field. The samples were packed into zirconia rotors with external diameter of 4 mm and the measurements were performed at a temperature of 23 \({}^{\circ}\)C.
## 2 Electro-Optical Sampling Principle of Differential Modulation Architectonic  
The architecture of the used SOA-MZI in a VPI simulator [26] is propounded in Figure 1. It explains, for the first time, the electro-optical up converter principle of the differential modulation based on a sampling SOA-MZI. We have previously demonstrated the up-conversion process based on a sampling method [11, 12]. Furthermore, the approach of the electro-optical SOA up-converter was explained in [23].  
The sampling signal is driven by an optical pulse clock (OPC) with a sampling frequency \(f_{s}\) is divided into two similar signals to the upper and lower arms of the SOA-MZI by the middle port (MP). Recently, we have used \(f_{s}=19.5\) GHz in [14].  
The proposed differential modulation relies on two electrical subcarriers in the MZI. In this scheme, two subcarriers at \(f_{IF}\), which have similar characteristics, are entered into the electrode of upper and lower SOAs leading to fine-tuning their bias currents. The IF signals are capable of regulating the carrier density of each SOA by their bias currents in the upper and lower arm, and then the sampling signal will be directly modulated by the IF signals in both arms. These two sampled signals are combined at the outer port of the SOA-MZI, beneficial to acquiring the mixed signal. The benefit of the differential modulation is to enhance the quality of the harmonics of the sampling signal and the power level of the replicas of the signal to be mixed. The sampled signal is considerably ameliorated at the outer port of the SOA-MZI because of enhancing the harmonic power of the sampling control signal in both arms, which preserves the identical amplitude of its harmonics regardless of the amounts of the positions of the harmonics \(n\). As we can monitor from the electrical spectrum of the signal to be mixed that the IF signal is up-converted from \(f_{IF}\) to \(nf_{s}\pm f_{IF}\).
## 4 GNC/FDIR System Design and Analysis  
The following sections address the Navigation, Control, and Guidance subsystems, including the Fault Detection, Isolation, and Recovery (FDIR) functions. These are critical elements in the capture of non-cooperative targets, guaranteeing that the relative position and attitude are known and controlled for a proper servicing of the client.
## 3 Results  
### Characterization of Mj-HSP60 and Mj-HSP67B2 in M. japonicus  
We identified two HSP genes (_Mj-HSP60_ and _Mj-HSP67B2_) in our 454 GS-FLX transcriptome analysis [33] that were composed of 1360 nucleotides (nt) and 511 nt, which comprised open reading frames encoding 330 and 149 amino acids, respectively (Figures 1A and 2A). _Mj-HSP60_ encoded a mature protein of 330 amino acids, 75 bp of 5\({}^{\prime}\) untranslated region (UTR) and 57 bp of 3\({}^{\prime}\) UTR, with a putative methionine initiation codon (ATG) beginning at 58 nt and a stop codon ending at 1224 nt. The SignalP Server (ExPASy) [34] predicted that the first 28 amino acids in the N-terminal region of the polypeptide chain would form a signal peptide sequence. We found that _Mj-HSP60_ included a chaperonin-like super family main domain, whereas a RHOD superfamily motif was detected in _Mj-HSP67B2_ (Figure 2A). The predicted molecular mass of the deduced amino acid sequence was 61 kDa, with an estimated isoelectric point (pl) of 5.74. _Mj-HSP60_ was identified by a BLAST search of the National Center for Biotechnology Information (NCBI) non-redundant (nr) database. To understand the evolutionary position of the _Mj-HSP60_, we undertook phylogenetic analysis using another 11 species of crustaceans. As shown in Figure 1B, the phylogenetic tree consisted of two clades involving 12 crustacean species. The _Mj-HSP60_ formed one main clade with other crabs (_Eriochexin sinensis, Scylla paramamosain_, and _Portunus trituberculosisatus_) and crayfish (_Cherax cainii_, _Cherax quadricariatus_, and _Cherax destructor_). The other clade was composed of shrimp species (_Macrobrachium ripponense_, _Macrobrachium rosenbergii_, _Peneus japonicus, Penaeus monodon_, and _Penaeus vannanei_). For clear annotation of _Mj-HSP67B2_, we examined the RHOD superfamily domain sequence (98 amino acids) using BLASTN searches of the nr database to detect sequences of other species with high similarity. We carried out pairwise alignment of _Mj-HSP67B2_ using EMBOSS alignment (EMBL-EBI, Cambridgeshire, UK) [35] with sequences identified in BLAST searches. The results showed 35.9-72.8% sequence identity, 54.4-81.6% similarity, and 4.9-10.5% gap percentage when compared with _HSP67B2_ from other species (Table 1). The _Mj-HSP67B2_ sequence revealed considerable identity (72.8%), similarity (81.6%), and gap percentage (4.9%) with _Peneus vannanei HSP67B2_. In addition, phylogenetic analysis of the _Mj-HSP67B2_ was carried out using data from various arthropod species, owing to deficient genomic information regarding the _HSP67B2_ in crustaceans (Figure 2B). The results showed that the two main clades were divided into Crustacea and Insecta, including mosquito and fly species. The _Mj-HSP67B2_ showed the closest phylogenetic relationship to _Penaeus vannanei HSP67B2_. Given these results from analysis of phylogenetic and pairwise sequence alignment comparisons, our transcript sequence from the transcriptome database was identified as _Mj-HSP67B2_.
# A Single Wearable Sensor for Gait Analysis in Parkinson's Disease: A Preliminary Study
## 2 Design of the Proposed System
### Extraction of the Activity Segments  
The pre-processed signals were filtered to extract the activity segments. An activity is defined as a movement of the wearable device associated with an ADL performed by the subject. In order to identify the activity segments, accelerometer data were segmented using a two-second sliding window with 50% overlap [30]. The sliding window was used to segment time series for activity segments extraction. We selected a window size of two seconds to ensure the capture of an activity and to exclude any type of pause performed by the subject. To distinguish between periods of user activity and rest, a measure that  
Figure 1: The used inertial wearable sensor and its position on the subject’s lower back.  
Figure 2: Block diagram of the proposed algorithm.  
includes the effect of signal variations in all three axes was required. To extract the activity segments, the signal magnitude area (\(SMA\)) and the energy (\(EN\)) are defined by equations:  
\[SMA=\sum_{i=1}^{N}\!\sqrt{Ax(t)^{2}+Ay(t)^{2}+Az(t)^{2}} \tag{1}\]  
\[EN=\sum_{i=1}^{N}\!\sqrt{|fft(Ax(t))|^{2}+|fft(Ay(t))|^{2}+|fft(Az(t))|^{2}}, \tag{2}\]  
where \(i\) is the current sliding window, \(N\) is the total number of sliding windows, \(Ax(t)\), \(Ay(t)\), and \(Az(t)\) denote accelerations along \(x\), \(y\), and \(z\) axes in time domain, and \(fft\) is the fast Fourier transform. If the \(SMA\) and \(EN\) values of the current sliding window exceed the 75% of the mean of the \(SMA\) and \(EN\), respectively, for at least three consecutive windows, activity is considered to have occurred. Conversely, values below the threshold mean the user is in a resting state. Subsequently, only activity with a duration higher than 10 s was used for further processing. Figure 3 shows the output of this step of the algorithm.
# Spatiotemporal Feature Enhancement for Lip-Reading: A Survey
## 4 Difficulties and Challenges
### Semantic Integrity  
Closely related to the challenge of visual ambiguity is the issue of semantic integrity in the recognition process. The dimensions of lip-reading images are typically limited, which creates obstacles in constructing complex feature enhancement networks capable of capturing deeper semantic meanings.  
While it is relatively straightforward to extract shallow spatial features, capturing comprehensive semantic features remains elusive. This limitation further exacerbates the challenges posed by visual ambiguities and misaligned features, as a lack of robust semantic representation can lead to inadequate distinctions between similar lip shapes. Therefore, developing strategies for the accurate and effective extraction of deep semantic features is another vital aspect of enhancing the integrity of spatiotemporal characteristics.
# Numerical Study on Dynamic-Stall Characteristics of Finite Wing and Rotor
## 3 Analyses and Discussion  
The dynamic-stall characteristics of a rotor would be influenced by spanwise flow, blade-tip vortex, and blade rotation. As a result, in order to clearly investigate the 3D effects on dynamic-stall characteristics, we decouple the influence factors of the 3D effects into three parts. First, the dynamic-stall characteristics of a finite wing were investigated to explore the influence of the wing-tip vortex on the characteristics. Second, the dynamic-stall characteristics of a finite wing with spanwise flow were investigated to explore the influence of the flow on the characteristics. Finally, the dynamic-stall characteristics of a rotor were investigated in forward flight to explore the synthetic influence of the vortex, spanwise flow, and blade rotation.