esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Spaceborne Target Detection Based on Yolov5 and Image Compression
## 3 Remote Sensing Target Detection
### Yolov5 Network
#### 3.1.1 Input Layer  
(1). Mosaic data augmentation  
The author of the Mosaic data enhancement proposal is also a member of the Yolov5 team. The data enhancement using a stitching effect is created from random zoom, random cropping, and random arrangement, and the detection effect is good for small targets.  
(2). Adaptive anchor box calculation  
Figure 2: The network structure diagram of Yolov5.  
In [PERSON]'s algorithm, there is an anchor box with an initial length and width for different data sets.  
In the network training, the network outputs the prediction box on the basis of the initial anchor box, and then compares it with the real box, calculates the gap between the two, and then updates it in reverse to iterate the network parameters. The anchor setting in the model file is the initial anchors box, as shown in Table 1.  
(3)Adaptive picture zoom  
In the commonly used target detection algorithms, different pictures are different in length and width, so the usually implemented method is to uniformly scale the original pictures to a standard size, such as the widely used 640\(\times\)640, and to then send them into the detection network.  
For rectangular pictures with a different length and width, Yolov5 is processed by minimizing the filling, using gray to fill the scaled picture to scale it to the standard size, and it is then input into the neural network.
# Effects of Temperature on the FT NIR Raman Spectra of Fish Skin Collagen
## 2 Materials and Methods  
The collagen gel used in the experiment was obtained from silver carp (_Hypophthalthys molitrix_) skin (FS collagen) Collagen was purchased from Collagen Active Science Sp. z o. o. Poznan, Poland.The FS collagen obtained by means of the method presented in [21] most probably exhibits a triple helix conformation which is unique to living organisms, which was verified by viscosity measurements and electrophoresis [3, 46, 47].  
Collagen native conformation was determined by means of temperature dependence of viscosity using the Ubbelohde viscometer, in the temperature range of 293-333 K. The denaturation temperature was established as (306.3 \(\pm\) 1.5) K [3, 24, 47]. The molecular weight characteristics found by electrophoresis showed two distinct lines at \(\sim\)130 and \(\sim\)200 kDa, related to \(\alpha\)- and \(\beta\)-chains, respectively [44]. The amino acid content can be found elsewhere [46].  
Collagen gel was dissolved in water and the acquired 10% solution was poured into a Petri dish and dried at room temperature. Obtained films were used in further studies. Raman spectra were recorded with a Bruker IFS66 FT IR spectrometer equipped with an FRA106 Raman module. The samples were excited by a diode pumped Nd:YAG laser operated at \(\lambda\) = 1064 nm with output power of \(\sim\)50 mW. Raman spectra were measured in the 303-403 K range of temperature using a Linkam cooling-heating stage in 180\({}^{\circ}\) geometry. The temperature range of approximately 300-400 K covers the temperature range expected in many biomedical applications as well as collagen stability against temperature [48].  
Moreover, the application of IR radiation permitted us to avoid any effect of UV radiation on collagen structure [49].  
The molecular structure and the temperature effects on the secondary structure of fish skin (FS) collagen were compared with bovine Achilles tendon (BAT) collagen purchased from Sigma-Aldrich, Poland. Its amino acid composition can be found in [50].
###### Abstract  
An aptamer-based electrochemical biosensor was successfully developed and applied in the rapid detection of pathogenic _Escherichia coli_ (_E. coli_) in licorice extract. The thiolated capture probes were firstly immobilized on a gold electrode, and then the biotinylated aptamer probes for _E. coli_ were introduced by hybridization with the capture probes. Due to the stronger interaction between the aptamer and the _E. coli_, a part of the biotinylated aptamers will dissociate from the capture probes in the presence of _E. coli_. The residual biotinylated aptamer probes can quantitatively bind with streptavidin-alkaline phosphatase. Subsequently, \(\alpha\)-naphthyl phosphate substrate was catalytically hydrolyzed to generate electrochemical response, which could be recorded by a differential pulse voltammetry. The dependence of the peak current on the logarithm of _E. coli_ concentration in the range from 5.0 \(\times\) 10\({}^{2}\) colony forming units (CFU)/mL to 5.0 \(\times\) 10\({}^{7}\) CFU/mL exhibited a linear trend with a detection limit of 80 CFU/mL. The relative standard deviation of 5 successive scans was 5.3%, 4.5% and 1.1% for 5.0 \(\times\) 10\({}^{2}\), 5.0 \(\times\) 10\({}^{5}\) and 5.0 \(\times\) 10\({}^{7}\) CFU/mL _E. coli_, respectively. In the detection of the licorice extract samples, the results obtained from the proposed strategy and traditional culture counting method were close to each other, but the time consumption was only \(\sim\)1/30 compared with the traditional method. These results demonstrate that the designed biosensor can be potentially utilized for rapid microbial examination in traditional Chinese medicine and relevant fields.  
aptamer; electrochemical; _Escherichia coli_; biosensor; licorice
## Appendix A  
In this Appendix, we prove Theorem 1. In the following, the infinity norm \(\|x\|_{\infty}\) of a vector \(x\ =\ (x_{1},x_{2},\cdots,x_{m})\in\mathbb{R}^{m}\) is defined as \(\max_{i}|x_{i}|\).  
**Lemma A.1.** Let \(f:\mathbb{P}_{m}^{*}\rightarrow\mathbb{R}\) be a function, and let  
\[\epsilon_{j}=\max\{|f(p_{\lambda_{1}})-f(p_{\lambda_{2}})|:\|\lambda_{1}- \lambda_{2}\|_{\infty}\leq 1,\ \ \lambda_{1},\lambda_{2}\in\Lambda_{j}(m,k)\},\ \ j\geq 0 \tag{38}\]  
If \(\sum_{j=0}^{\infty}\epsilon_{j}<\infty\), then \(f\) is uniformly continuous on \(\mathbb{P}_{m}^{*}\).  
_Proof._ We show there exists \(B>0\) such that  
\[\sup\{|f(q_{1})-f(q_{2})|:\|q_{1}-q_{2}\|_{\infty}\leq k^{-J},\ \ q_{1},q_{2}\in \mathbb{P}_{m}^{*}\}\leq B\sum_{j=J}^{\infty}\epsilon_{j},\ \ J\geq 0\]  
from which the uniform continuity follows. It can be shown that the following two properties hold.  
* **(p.1):** For each \(j\geq 0\) and each pair of distinct types \(\lambda_{0},\lambda\in\Lambda_{j}(m,k)\), the following is true. Letting \(I=m\|\lambda_{0}-\lambda\|_{\infty}\), there exist types \(\lambda_{1},\lambda_{2},\cdots,\lambda_{I}\) in \(\Lambda_{j}(m,k)\) such that \(\lambda_{I}=\lambda\) and \[\|\lambda_{i}-\lambda_{i-1}\|_{\infty}\leq 1,\ \ i=1,2,\cdots,I\] (In other words, we can travel from \(\lambda_{0}\) to \(\lambda\) via a path in \(\Lambda_{j}(m,k)\) consisting of \(I\) terms, with successive terms no more than distance \(1\) apart in the infinity norm.)
* **(p.2):** There is a positive integer \(M\) for which the following is true. For each \(j\geq 1\) and each \(\lambda_{0}\in\Lambda_{j}(m,k)\), there exist types \(\lambda_{1},\lambda_{2},\cdots,\lambda_{M}\) in \(\Lambda_{j}(m,k)\) such that \(\lambda_{M}/k\in\Lambda_{j-1}(m,k)\) and \[\|\lambda_{i}-\lambda_{i-1}\|_{\infty}\leq 1,\ \ i=1,2,\cdots,M\]  
it follows that  
\[\|\lambda_{i}-\mu_{i}\|_{\infty}\leq 1,\ \ i=1,2,\cdots,k \tag{39}\]  
and by Lemma 1 we have  
\[H(S(\lambda))=\sum_{i=1}^{k}H(S(\lambda_{i}))+\log_{2}N_{1}\]  
\[H(S(\mu))=\sum_{i=1}^{k}H(S(\mu_{i}))+\log_{2}N_{2}\]  
where \(N_{1},N_{2}\) are positive integers \(\leq k!\). The latter two equations imply  
\[|f(p_{\lambda})-f(p_{\mu})|\leq k^{-1}\sum_{i=1}^{k}|f(p_{\lambda_{i}})-f(p_{ \mu_{i}})|+\log_{2}(k!)/k^{j}\]  
from which, using Inequality (39),  
\[|f(p_{\lambda})-f(p_{\mu})|\leq\epsilon_{j-1}+\log_{2}(k!)/k^{j}\]  
We conclude that  
\[\epsilon_{j}\leq\epsilon_{j-1}+\log_{2}(k!)/k^{j},\ \ j\geq 1\]  
from which it follows that \(\sum_{j=0}^{\infty}\epsilon_{j}<\infty\). Applying Lemma A.1, we can now say that \(f\) is uniformly continuous on \(\mathbb{P}_{m}^{*}\).
## 3 Isolation Effect
### Comparison with a Normal Spring-Mass Isolation Platform  
The isolation effectiveness of the isolation platform with SLSs and springs are compared, which is shown in Figure 9. For two cases, \(n=2\), \(\theta=\pi/4\), \(l=0.2\) (case 1) and \(n=3\), \(\theta=\pi/6\), \(l=0.2\) (case 2), the performances of isolation platform with springs and SLSs are obtained and shown in Figure 9.  
From Figure 9, it can be seen that the isolation platform with springs has two obvious resonant peaks are about 100 and 110 rad\(\cdot\)s\({}^{-1}\) expect zero. While for the two cases of the isolation platform using SLSs, the fundamental natural frequency is much smaller than the one using springs. For case 1 as \(n=2\), \(\theta=\pi/4\) and \(l=0.2\), the first resonant peak is at about 50 rad\(\cdot\)s\({}^{-1}\), and for case 2 as \(n=3\), \(\theta=\pi/6\) and \(l=0.2\), the first resonant peak is at 25 rad\(\cdot\)s\({}^{-1}\). Additionally, the values of displacement transmissibility for the two cases of the platform with SLSs are much smaller than the case with springs in a broad frequency domain.
# A Dynamic Restructuring Algorithm Based on Flexible PON Slices
## 4 Results and Analysis
Figure 5: (a) Improved performance of DRA compared to DGA and DONUSA with 8 OLTs; (b) improved performance of DRA compared to DGA and DONUSA with 16 OLTs. performance than DONUSA in the first experiment, this is because when N = 32, M = 8 or N = 64, M = 16, the number of nodes in each subgraph (\(K\)) is only 4 and DONUSA is greatly affected by the randomness of initialization when the number of nodes in each subgraph is small. Only with the increase in the number of ONUs can the performance of DONUSA be gradually stabilized [21]. In addition, in order to test the influence of the number of subgraphs on the performance of the algorithms, the number of ONUs is set at 512 and the simulation is carried out for the case of a varying number of OLTs (4, 8, 16, 32, 64, 128, 256). As shown in Figure 6, DRA also achieves better performance than DGA and DONUSA under different OLT numbers.
## 3 Results and Discussion
### Impact of the Condenser Height
These findings indicate that a higher condenser position generally enhances the PCTCS's energy efficiency. The operational principles of the PCTCS can be understood in the context of a specialized gravity thermosyphon, which is propelled by the dual forces of gravity and the pump. In a gravity thermosyphon, the performance is significantly dictated by the elevation of the condenser. Notably, a higher condenser position leads to an enlarged liquid column in the descending tube, generating a greater gravitational driving force. Similarly, within the PCTCS, elevating the condenser results in a stronger gravitational driving force. This, in turn, decreases the need for an additional driving force from the pump, contributing to the conservation of pump power and subsequently enhancing the PCTCS's energy efficiency. This finding aligns with the experimental results presented by [PERSON] [47]. Nevertheless, it is imperative to highlight that the influence of the condenser's positioning on the PCTCS's efficiency, while present, is relatively subtle--a detail also emphasized in [PERSON]'s study [46]. Additionally, with the condenser elevated by 1 m, the degree of subcooling at the first evaporator inlet was recorded at 13.17 \({}^{\circ}\)C, lower than the 14.51 \({}^{\circ}\)C at the same elevation and the 17.06 \({}^{\circ}\)C at 1 m lower. Intriguingly, when the condenser was raised by 1 m, the condenser outlet was a two-phase state, thus eliminating subcooling. In contrast, condensers at the same level and 1 m lower registered subcooling degrees of 0.78 \({}^{\circ}\)C and 4.59 \({}^{\circ}\)C, respectively. Furthermore, an elevated condenser position correlated with a higher Tdt and T\({}_{\text{cpu}}\). Specifically, when the condenser was 1 m higher, the average temperatures of the two CPUs increased by 0.46 \({}^{\circ}\)C and 0.90 \({}^{\circ}\)C compared to those at the same elevation and 1 m below. Moreover, the CPU temperature distribution remained relatively uniform across varying condenser elevations, implying a minimal impact of condenser height on CPU temperature uniformity. To summarize, while a raised condenser position generally enhances the PCTCS's efficiency, it slightly compromises the temperature performance, albeit these differences can be negligible in actual applications.
# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
## 7 UOC Near-Future Network Scenarios and Research Challenges
### Proposed Near-Future Data Communication Network Scenario
Long-distance multi-hop UOC data links use advanced relaying techniques, such as optical amplify and forward (OAF) and optical regenerate and forward (ORF). UOC systems based on polarization shift keying modulation and/or OAM are used to address the question of reliability for optical communication systems. The optimization of UOC-based NGNs is also being studied for cellular backhaul. Figure 41: Next generation UOC network: terrestrial optical fiber network (TOFN), optical ground station (OGS), lower-earth-orbit control station (LEOCS), medium earth orbit (MEO), earth observation satellite (EOS), and HAP deep-space satellite. Figure 42: UOC network with DWDM access technique for the NGN. However, to improve the number of network users and to fully utilize the available optical channel capacity, network designers must ultimately provide a low-cost service.
# A Brief Review of Multipath TCP for Vehicular Networks
## 6 Multipath Routing in Vehicular Networks
### Extensions of Ad Hoc Routing Protocols  
There are many single path routing protocols such as ad hoc on-demand distance vector (AODV) [113], dynamic source routing (DSR) [114], DSDV (dynamic destination-sequenced distance-vector) [115], and TORA (temporally-ordered routing algorithm) [116]. In order to enhance the connectivity between end hosts and transmission efficiency, many multipath routing protocols that extend AODV and DSR were proposed. For example, ad-hoc on-demand multipath distance vector (AOMDV) [9] and multipath source routing (MSR) [8]. In [117], [PERSON] et al. compared the performance of AOMDV with AODV. AOMDV outperforms AODV in throughput, packet loss rate, and end-to-end delay. Most importantly AOMDV is less influenced by vehicle speed and density compared to AODV. MSR extends DSR's route discovery and route maintenance mechanism. As part of the maintenance mechanism, a probing mechanism is employed to balance the load between paths based on the measurement of RTT. In [118], based on the advantages of AOMDV, such as fault-tolerant and load balance, an improved AOMDV with speed metric namely S-AOMDV was proposed.  
Figure 5: Multipath routing in vehicular networks.
# Measurements of Room Acoustic and Thermo-Hygrometric Parameters--A Case Study +
## 3 Data Analysis and Results
### Regression Analysis Using R  
After determining the variations of the acoustic and thermo-hyrorometric parameters, a regression analysis was performed on both data sets using R, a programming language, to investigate the relationship between the acoustic parameters and the thermo-hyrorometric parameters. K-fold cross-validation was used, which is an effective method for validating machine learning models by dividing the training set into K subsets and then validating the data for each subset, ultimately averaging the K validation results to evaluate the fit of the model. In this study, five-fold cross-validation method to conduct a regression analysis on the acoustic and thermal humidity parameters has been used. k = 5 was determined from the empirical data, which is a relatively acceptable parameter value as it can effectively divide the data in order to more accurately assess the generalisation performance and prediction performance of the model [27]. The univariate regression analysis produced the best results after testing. Temperature and humidity were independently regressed for the acoustic parameters, and the results are displayed in Appendix A. The coefficient is the regression coefficient, p is the p-value, RMSE is the root mean square error, R\({}^{2}\) is the R-squared, and MAE is the mean absolute error. The p-value is the probability of occurrence, given that there is no linear relationship between the two variables. As a result, when the \(p\)-value is minimal, i.e., \(p\)-value < 0.05, a linear relationship between the two variables can be considered to exist. With the exception of IACC at 125 Hz; T\({}_{30}\), LE, and LF at 250 Hz; T\({}_{30}\) and IACC at 500 Hz; and C\({}_{50}\), C\({}_{80}\), and D\({}_{50}\) at 2000 Hz and 4000 Hz, there is a linear relationship between parameters and temperature, and the smaller the \(p\)-value, the more pronounced this linear relationship, as shown in Table A1. From Table A2, it can be concluded that there is a linear relationship between parameters and humidity with the exception of T\({}_{30}\), LE, and LF at 250 Hz; T\({}_{30}\) and IACC at 500 Hz; and C\({}_{50}\), C\({}_{80}\), and D\({}_{50}\) at 2000 Hz and 4000 Hz. A positive regression coefficient indicates a positive association, whereas a negative value indicates a negative correlation. The absolute value of the coefficient indicates the magnitude of the change in the dependent variable due to a unit change in the independent variable. A visualisation with commands, Figure 11 displays examples of linear and non-linear, positive, and negative correlation scatterplots out of a total of 114 produced. The R-squared value measures the explanatory power of the model and indicates the percentage of variance in the dependent variables (acoustic parameters) explained by the independent variables (temperature and humidity). R-squared values range from 0 to 1, with larger values indicating better regression equations. Temperature serves as the independent variable (same as humidity).
# Matrix Stiffness Influences Tubular Formation in Renal Tissue Engineering
## 3 Results
### Tube Formation on Agarose  
After 24 h of culture, the tubular formation was scored according to the categories listed in Figure 3, following our previous findings [11]. Figure 4A,B illustrates the percentage of tubes in the function of the agarose concentrations. We observed that better tube formation (2P and 3P) occurred in the agarose mold with higher concentrations (3% and 5%), while either no tubes or small tubes (2N and N) formed in agarose with the lowest concentrations (1%), and no significant differences were observed between the different tube status in 2% agarose molds. When agarose concentration increases, we observed that the number of 2N tubes remained constant (no significant difference) at lower concentrations (1% and 2%) but dramatically decreased at concentrations between 2% and 3% and remained low at concentrations between 3% and 5%. Similarly, the number of N tubes rapidly decreased (from 1% to 2%), and remained low when the agarose concentration increased (from 2% to 5%). We did not observe significant differences in the number of tubes remaining in the mold (P tubes) at low concentrations (1% and 2%, 28.6% and 34.5%, respectively) and high concentrations (3% agarose and 5% agarose, 4% both). However, differences between both groups were significant. The number of 2P tube formations increased and  
Figure 2: Elastic moduli of (**A**) agarose and (**B**) polydimethylsiloxane (PDMS). (**C**)The elastic moduli of the agarose substrate at 3% and 5% overlaps with the PDMS substrate generated in a 1:30 and 1:20 ratio.  
then decreased with stiffness, with a peak at 3% agarose (21%). The percentage of 3P tubes quickly increased with the agarose concentration from 2 \(\pm\) 3.4% (on agarose 1% mold) to reach 74.5 \(\pm\) 5.4% on 3% agarose mold, and remained constant (65.8 \(\pm\) 11.7%, with no significant difference between 3% and 5%).  
Figure 4: Percentage of tube formation on agarose (**A**,**B**) and PDMS (**C**,**D**) molds. Data are represented in function of the (**A**,**C**) substrate composition and (**B**,**D**) the tube formation’s grade. Symbols indicate the \(p\)-value of the ANOVA test: \(p\) < 0.05 (*), \(p\) < 0.005 (**), \(p\) < 0.0005 (\(\uparrow\)), \(p\) < 0.00005 (\(\uparrow\)), \(p\) < 0.00005 (\(\uparrow\)), \(p\) < 0.000001 (\(\uparrow\)), and no differences (nd). A compact letter display is used to indicate non-significant differences from post hoc analysis (where a similar letter indicates no significant differences).  
Figure 3: The efficiency of tube formation was graded after 24 h of culture along the characteristics shown in the panels. Tube formation was scored by grades (from low to high) as 2N (\(--\)), 1N (\(-\)), 1P (\(+\)), 2P (\(++\)) and 3P (\(++\)).
## 5 Identification Procedures and Validity
### Measurement to Identify Geometric Deviations  
To identify 12 geometric deviations based on the eccentricity of circular trajectory controlled by simultaneous three-axis motions, the following six measurements by means of the ball bar are indispensable, as shown in Figure 6.  
Step 1: Four measurements, which are the B axis X-direction, B axis Y-direction, C axis axial direction and C axis radial direction, are conducted at the condition of \(L_{B}\), \(Z_{\text{C}}\) and \(R_{\text{B}}\).  
Step 2: The C axis radial measurement is conducted for the second time at the condition of \(L_{B}\), \(Z^{\prime}_{\text{C}}\) and \(R_{\text{B}}\).  
Step 3: The C axis radial measurement is conducted for the third time at the condition of \(L_{B}\), \(Z^{\prime}_{\text{C}}\) and \(R^{\prime\prime}_{\text{B}}\).  
As a result, a ball bar measurement should be conducted once in the B axis X-direction, B axis Y-direction, C axis axial direction, and three times in C axis radial direction. After that, including the squareness of translational axes, 12 geometric deviations can be calculated by Equations (17)-(28).
# Deep Anomaly Detection for In-Vehicle Monitoring--An Application-Oriented Review
## 3 Publicly Available Datasets
### Real-World Datasets
#### 3.1.2 Real-World Anomalies  
Motivated by the limitations of previous datasets, UCF-Crime [40] was developed as a new large-scale dataset to evaluate video anomaly detection. It is composed of 1900 untrimmed videos of real-world surveillance footage, extracted from the internet, with an average length of 4 min each. It includes 13 types of anomalous events with a high impact on public safety, such as abuse, burglary, shoplifting and shooting. A comparison between a normal frame and a shooting sequence is displayed in Figure 4a,b. UCF-Crime contains annotated bounding boxes of anomalous regions in one image per 16 frames of each abnormal video. A considerable amount of available data was essential for the development of weakly supervised strategies.  
XD-Violence [59] was originally released to develop a large-scale and multi-scene dataset for violence detection and classification. However, this task can be understood as a subset of anomaly detection, and the dataset can be used to benchmark new video anomaly detection methods. Furthermore, it contains audio-visual signals, allowing for the research on multi-modal solutions for this problem. XD-Violence consists of 4754 weak-labelled untrimmed videos with audio, which were collected from both films and YouTube. This dataset embraces a variety of scenarios and anomalies, for instance, rioting, car accidents and explosions, as shown in Figure 4c,d.