esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Design and Methodology
### Response Surface Methodology (RSM)
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Experiment No.**} & \multicolumn{3}{c}{**Input Variables**} & \multicolumn{3}{c}{**Output Responses**} \\ \hline  & & & & & & & & & \\ \hline \#1 & 45 & 13 & \(-1\) & \(10.916\pm 0.91\) & 0 & - & \(1.924\pm 0.23\) & 3.461 \\ \#2 & 60 & 10 & 0 & \(8.400\pm 0.61\) & \(3.056\pm 0.22\) & \(2.748\) & \(0.591\pm 0.12\) & 6.000 \\ \#3 & 50 & 10 & 0 & \(10.946\pm 0.88\) & \(4.323\pm 0.32\) & \(2.531\) & \(0.779\pm 0.18\) & 5.000 \\ \#4 & 50 & 10 & 0 & \(10.270\pm 0.98\) & \(5.710\pm 0.35\) & \(1.798\) & \(0.490\pm 0.11\) & 5.000 \\ \#5 & 50 & 10 & \(-2\) & \(4.436\pm 0.35\) & \(9.653\pm 0.54\) & \(0.459\) & \(0.574\pm 0.17\) & 5.000 \\ \#6 & 50 & 10 & 2 & \(6.696\pm 0.61\) & \(6.440\pm 0.78\) & \(1.039\) & \(0.025\pm 0.03\) & 5.000 \\ \#7 & 55 & 7 & 1 & \(10.243\pm 0.75\) & 0 & - & \(1.644\pm 0.29\) & 7.857 \\ \#8 & 55 & 7 & \(-1\) & \(7.600\pm 0.47\) & \(3.086\pm 0.33\) & \(2.462\) & \(0.484\pm 0.12\) & 7.857 \\ \#9 & 45 & 7 & 1 & \(8.103\pm 0.58\) & \(2.696\pm 0.29\) & \(3.004\) & \(0.600\pm 0.22\) & 6.428 \\ \#10 & 45 & 7 & \(-1\) & \(9.556\pm 0.79\) & \(3.926\pm 0.21\) & \(2.433\) & \(0.631\pm 0.25\) & 6.428 \\ \#11 & 55 & 13 & \(-1\) & \(7.393\pm 0.54\) & 0 & - & \(0.911\pm 0.11\) & 4.230 \\ \#12 & 50 & 10 & 0 & \(13.453\pm 0.113\) & \(4.890\pm 0.42\) & \(2.751\) & \(1.152\pm 0.32\) & 5.000 \\ \#13 & 50 & 16 & 0 & \(9.953\pm 0.94\) & 0 & - & \(1.541\pm 0.13\) & 3.125 \\ \#14 & 55 & 13 & 1 & \(10.023\pm 0.86\) & \(5.590\pm 0.56\) & \(1.793\) & \(0.474\pm 0.28\) & 4.230 \\ \#15 & 40 & 10 & 0 & \(6.510\pm 0.66\) & \(5.066\pm 0.44\) & \(1.284\) & \(0.145\pm 0.22\) & 4.000 \\ \#16 & 50 & 4 & 0 & \(4.720\pm 0.53\) & \(3.506\pm 0.63\) & \(1.346\) & \(0.121\pm 0.15\) & 12.500 \\ \#17 & 45 & 13 & 1 & \(10.910\pm 0.87\) & 0 & - & \(1.921\pm 0.16\) & 3.461 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental layout and multi-performance results.
# Automated Bone Age Assessment with Image Registration Using Hand X-ray Images
## 6 Results and Discussion
Only the results for the first 40 epochs are shown to produce good visualization. performance is produced by the Inception V3 model, followed by ResNet and DenseNet with \(MAE\) values of 9.774, 10.283, and 10.557, respectively. Note that the proposed method prediction is around 1.5 months better when compared to the second best method, Inception V3. However, the prediction performance gap between Inception V3 with ResNet is just around 0.5 months. Thus, our method has produced relatively significant improvement as compared to the other networks. Besides, ResNet has produced a better \(MAE\) compared to the DenseNet, but its \(MSE\) is significantly big as compared to the DenseNet. This is because ResNet generally returns good bone age prediction, but it also returns a few bad predictions that are quite far when compared to the annotated ground truth age. Hence, its squared error is big as this metric punishes big error heavily as compared to the \(MAE\) metric. It is also interesting to note the top four methods are produced by networks with a total number of parameters that are bigger than 10,000,000. The best performed lightweight model is MobileNet V1, with \(MAE\) equal to 10.886 and \(MSE\) equal to 190.349. Moreover, MobileNet V1 returns the best performance compared to the other MobileNet family architectures. There is a trend among the MobileNet family models where a bigger number of parameters used in the network will produce a better prediction performance. Similarly, the performance of ShuffleNet V2 is also better when compared to ShuffleNet V1, where it uses more parameters, which is 5,380,761 as compared to the ShuffleNet V1, with just 936,457 parameters. However, the convolution architecture also plays an important role in determining the network performance, where SqueezeNet with expand and squeeze scheme has produced a lower \(MAE\) when compared to ShuffleNet V1, even though it uses less memory of just 735,939 parameters. Nevertheless, the lightweight model performance is generally lower as compared to the standard deep learning models, except for the VGG-19 network. In fact, it is the biggest model with 38,911,041 parameters, but its architecture consists of just a single network flow without applying any residual or branching operations. Thus, it cannot learn well the regression features and produces relatively weak predictions with \(MAE\) of just 14.028. The best performed model without applying a residual scheme is Inception V3, but it utilizes a wide network architecture with four parallel branches of different convolution sizes.
## 3 Results and Discussion
### Flammability Test  
A flammability test was performed on the sample to determine its burning behavior. Because the sample was not coated with any fire retardant, only the vertical flame test was chosen for the flammability test. After the initial flame application, the burning duration for 5-ply is the longest (3.7 s), and the shortest for 9-ply (2.9 s). The sample burning time did not change significantly after the second flame application. According to Table 4, 7-ply has the longest burning duration (1.4 s), while 9-ply has the shortest (1.2 s). The overall burning time for 5-ply is 5 s, followed by 4.6 s and 4.1 s for 7-ply and 9-ply, respectively. Figures 10 and 11 show char formation on the composite surfaces of 5-, 7-, and 9-ply woven bamboo panels during combustion, but not burning. There was no melt dripping from the composite samples. These characteristics allowed the composites to be classified as V-0 because the burning stopped within 10 s after two ten-second flame applications to the test bar. The number of ply had an effect on the flammability of the sample, which can be seen directly based on the duration of the burning time. Samples with higher numbers of ply have higher numbers of gluelines. For instance, 5-ply has four gluelines, 7-ply has six gluelines and 9-ply has eight gluelines. As PF resin is less flammable than the woven bamboo mat, therefore, the more gluelines, the lesser burning time of the samples.
## 3 A Comprehensive Monitoring System for Landslides in 'Space-Sky-Ground-Underground'
### Temperature distribution inside inclinometer tubes B1, B3, and B5
As shown in Figure 21, the stress borne by the landslide soil at a depth of 6 m from the ground surface on the day of the optical cable fracture reached over 3800 \(\mu\)e, indicating greater stress at the 7 m depth where the fracture occurred. In summary, we infer that the soil at a depth of 12 m from the ground surface became viscous due to rainfall and accumulated water, forming a sliding zone. The area at a depth of 7 m from the ground surface contains rubble and soil, and the shear force exerted by the sliding zone on the soil caused irreversible mechanical damage to the monitoring optical cable above, leading to the breaking of the optical cable. Therefore, it is necessary to consider the particular climate conditions of the subsequent landslide monitoring in the Three Gorges Region. By combining conventional techniques with drilling data, InSAR technology and conventional sensors can estimate the deformation value of landslides to grasp the balance point between sensor sensitivity and tensile capacity. In this way, the strength of sensors can be increased and small deformations inside the landslide can be accurately captured to ensure real-time acquisition of monitoring data from multiple underground landslide sites. Figure 20: Landslide-temperature-monitoring data.
## 4 Control Cases
### Water Tank Controller  
The main goal of the water tank controller is to maintain the level of water in a tank and the activation of the valve to keep the tank full, preventing the water from draining out of the tank. The diagram of the operation of this problem is shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Iteration** & **HMR** & **Low** & **Medium** & **High** \\ \hline Low & Low & - & - \\ Medium & - & Medium & - \\ High & - & - & High \\ \hline \hline \end{tabular}
\end{table}
Table 1: Rules for theType-1 and interval Type-2 fuzzy system.  
Figure 5: Input and output of the interval Type-2 fuzzy system.  
Water enters the tank from the top at a rate proportional to the voltage, \(V\), applied to the pump. The water leaves through an opening in the tank base at a rate that is proportional to the square root of the water height, \(H\), in the tank. This controller uses fuzzy logic to control the valve that determines how fast the water can enter the tank to maintain the desired level of water. The presence of the square root in the water flow rate results in a nonlinear plant. The mathematical model of the water tank can be represented as a differential equation and is given by Equation (6):  
\[\frac{d}{dt}Vol=A\ \frac{dH}{dt}=bV-a\sqrt{H}. \tag{6}\]  
Equation (6) describes the height of water \(H\) as a function of time, due to the difference between flow rates into and out of the tank.  
_Vol_ is the volume of water in the tank  
\(V\) is the voltage applied to the pump  
\(A\) is the cross-sectional area of the tank  
\(b\) is a constant related to the flow rate into the tank  
\(a\) is a constant related to the flow rate out of the tank  
This controller has two inputs and one output; the first input is the level, which has three Gaussian membership functions with linguistic variables called high, okay, and low. The second input is the rate, which has three Gaussian membership functions with linguistic variables of negative, none, and positive. The output is the valve, which has five triangular membership functions with linguistic variables of close fast, close low, no change, open slow, and open fast. This controller is of the Mamdani type and uses five rules, which are presented in detail in Table 2.  
The rules that are outlined in Table 2 are those that maintain control of the opening of the valve and with this have leveled the water tank. Figure 7 shows the structure of the fuzzy system for this controller.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Rule Number**} & \multicolumn{3}{c}{**Inputs**} & **Output** \\ \cline{2-5}  & **Level** & **Operator** & **Rate** & **Valve** \\ \hline
1 & okay & - & - & no change \\
2 & low & - & - & open fast \\
3 & high & - & - & close fast \\
4 & okay & and & positive & close slow \\
5 & okay & and & negative & open slow \\ \hline \hline \end{tabular}
\end{table}
Table 2: Rules for the water tank controller.  
Figure 6: Diagram of the water tank controller.
## 4 Results and Discussion
### Hydrodynamic Emulsion Particle Diameters and Particle Charge Measurements  
Mean particle diameters of the TM-nanoemulsions were measured by the DLS method, and the results are compiled in Table 1. Solutions with 1 and 2 %/v/v essential oil had diameters below 100 nm demonstrating formation of nanoemulsion. However, emulsion prepared with 3 %v/v essential oil had diameter of 165 nm, which is in the range of microemulsions. By increasing the concentration of nanoemulsion from 1 to 3 %v/v, the particle size increased from 50 to 165 nm (\(p>0.05\)). Because of the higher number of particle collisions and coalescence during emulsification, increasing thyme oil concentration led to increased mean droplets sizes. Because of the lower particle size of the 1 %v/v nanoemulsion, it was used as the drug model in this study.  
Polydispersity index (PDI) values are also given in Table 1. PDI shows the uniformity of particle diameter in an emulsion. By increasing the value of polydispersity, the uniformity of nanoemulsion particle size decreases [47]. PDI is defined as the ratio of the standard deviation from the mean particle diameter to the mean particle diameter [47]. PDI is dimensionless, and values smaller than 0.07 are seldom observed except for highly monodisperse systems [48]. PDI values higher than 0.7 are assigned for samples with a very vast size distribution and are not appropriate for DLS analysis [49]. Polydispersity values near 1.0 indicate a polydisperse system [50]. TM-nanoemulsions had low PDI values which indicate their stability and uniformity.
# An Efficient Retrieval Technique for Trademarks Based on the Fuzzy Inference System
## 5 Experimental Results and Discussion
### Discussion  
There are some limitations of the proposed technique. These above-mentioned experiments raised the idea that the proposed technique has surpassed the overall performance of the other competitive approaches; however, it does not perform well in the cases of classes 2, 4, 5, 8, 9, and 12, especially for classes 2 and 5. Therefore, we can nevertheless confirm that the proposed technique is slightly sensitive to the structural changes of tree and non-rigid body deformation. The two example images from classes 2 and 5 are shown with the degree of variant in Figure 14. The first three columns of Figure 14 show the images of the structural changes of the tree, and the other three columns show the images of the non-rigid body deformation. In the cases of test classes 2 and 5, the BESs of our system are less than those of ZMEG at 8% and the ANMRR scores are also slightly less than those of the ZMEG at 0.07. This indicates that the proposed technique is not sufficiently capable of handling to the structural changes of the tree or significantly deformed images. Nevertheless, an aim of the TIR system is to help the user to find visually similar images from the database such as the types of invariance of rotation, scaling, and translation, as well as rigid body deformation. We further observe the retrieval results of our system for the query image from class 5, as shown in Figure 15. The result shows that, although only 13 of the top 32 images in the retrieved result are relevant images, the shape of the bat is similar in the first 11 relevant images. As a consequence, we speculated that the proposed technique appears to be consistent with the aim of TIR system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approach** & **Image Representation** & **Similarity Measurement** & **Full Retrieval Time** \\ \hline Proposed technique & \(1.65\) & 0.04 & 1.69 \\ ZMEG [15] & 254.87 & 0.001 & 254.871 \\ ZMLDP [12] & 325.04 & 0.021 & 325.061 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of computation times in the retrieval process (Unit: ms).  
Figure 14: Various images within (**a**) Class 2 and (**b**) Class 5.
###### Abstract  
Slow and progressive loss of retinal ganglion cells (RGCs) is the main characteristic of glaucoma, the second leading cause of blindness worldwide. Previous studies have shown that impaired mitochondrial dynamics could facilitate retinal neurodegeneration. Mitochondrial dynamics are regulated directly (fission) or more indirectly (fusion) by dynamin-like protein 1 (DNML1). Therefore, DNM1L might be a promising target for an antibody-based approach to treat glaucoma. The consequences of targeting endogenous DNM1L by antibodies in a glaucoma animal model have not been investigated yet. Here, we show that the intravitreal application of an anti-DNM1L antibody showed protective effects regarding the survival of RGCs and their axons in the retinal nerve fiber layer (RNFL). Antibody treatment also improved retinal functionality, as observed by electroretinography (Ganzfeld ERG). Western blot analysis revealed altered DNM1L phosphorylation and altered expression of proteins related to apoptosis suggesting a decreased apoptosis rate. Mass spectrometry analysis revealed 28 up-regulated and 21 down-regulated proteins (\(p<0.05\)) in both experimental groups. Protein pathway analysis showed that many proteins interacted directly with the target protein DNM1L and could be classified into three main protein clusters: Vesicle traffic-associated (NSF, SNCA, ARF1), mitochondrion-associated (HSP9A, SLC25A5/ANT2, GLUDI) and cytoskeleton-associated (MAP1A) signaling pathway. Our results demonstrate that DNM1L is a promising target for an antibody-based approach to glaucoma therapy.  
glaucoma; antibodies; neuroprotection; neurodegeneration; discovery proteomics; dynamin-related protein 1; DRP1; DNM1L; animal model +
Footnote †: journal: Molecular Sciences
## 1 Introduction  
Parkinson's disease (PD) is a chronic, progressive disease mainly affecting middle-aged and elderly people. It is characterized by tremors, rigidity, decreased movement, abnormal postural reflex, and autonomic nervous dysfunctions. It is the second most common neurodegenerative disease in the world after Alzheimer's disease and affects approximately 1% of adults over age 60 [1]. The main pathological changes of PD include the loss of dopaminergic neurons and the formation of Lewy bodies in the substantia nigra of the midbrain. The pathogenesis of PD is unclear, but mitochondrial dysfunction, oxidative stress, altered protein handling, and inflammation may contribute to nigral dopaminergic cell death [2].  
At present, levodopa is still the first choice for PD treatment. However, with progression of the disease, long-term levodopa treatment may show decreased efficacy and cause -symptom fluctuations and motor complications. Studies have suggested that 8-45% of PD patients experienced dyskinesia after 4-6 years of treatment [3; 4; 5] with a disability as high as 43% [6]. It is believed that the occurrence and development of LID are related to the inherent lesions of the nigrostriatal regions and the pulsatile delivery of levodopa, involving a variety of neural signaling pathways and changes in brain network electrophysiological activities [7]. At present, the exact mechanism of LID is not clear, and effective treatment strategies are also lacking in clinical practice.  
Circular RNA is a newly discovered non-coding RNA with a closed circular structure, which is resistant to exonuclease and abundant in the whole transcriptome. The functions of most circRNAs remain unexplored, but many circRNAs exert important biological functions by acting as microRNAs or protein inhibitors, to regulate protein functions or to be translated [8]. CircRNAs are evolutionarily conserved and are more stable than linear RNAs, so circRNAs have enormous potential to be diagnostic and prognostic biomarkers [9]. Studies have shown that circRNAs are closely related to brain development [10], nervous system tumors [11], Alzheimer's disease [12], and PD [13]. For example, [PERSON] et al. found that circSLC8A1, which carries seven binding sites for miR-128 and is strongly bound to the microRNA effector protein, Ago2, may affect oxidative stress in PD by regulating miR-128 [13].  
There is growing evidence suggesting the potential role of circRNAs in nervous system disease. However, there are no reports on the role of circRNAs in LID. Here we studied the circRNA expression profile of the striatum of LID rats by high-throughput sequencing to identify circRNAs related to LID for further study.
# Tempo and Metrical Analysis by Tracking Multiple Metrical Levels Using Autocorrelation
## 1 Introduction  
Detecting tempo in music and tracking the evolution of tempo over time is a topic of research in Music Information Retrieval (MIR) that has been extensively studied in recent decades. Recent approaches based on deep learning have contributed to an important progress in the state of the art [1; 2]. In this paper, we present a method that relates to a more classical approach based on signal processing and heuristics-based data extraction. This classical approach generally detects in a first step the temporal repartition of notes, leading to an accentuation curve (or onset detection curve) that is further analyzed, in a second step, for periodicity estimation.  
The new proposed method is aimed at improving on those successive steps constituting the classical approach. First, a method for accentuation curve is developed, based on a detection of bursts of energy localized in time and frequency. This enables better emphasis of notes in dense polyphonic texture, while ignoring spectral fluctuation produced by vibrato and tremolo. Second, periodicity detection is performed using an improved version of autocorrelation function. Finally, hierarchical metrical structures, composed of a large set of periodicities in pairwise harmonic relationships, are tracked over time in parallel. In this way, the metrical structure can be tracked even if the rhythmical emphasis switches from one metrical level to another. A selection of core metrical levels enablesthe estimation of meter and tempo. Moreover, the tracking along many metrical levels in parallel enables a detailed description of the dynamic evolution of the metrical structure: not only how the whole structure speeds up or slows down with respect to global tempo, but also how individual metrical levels might be emphasized at particular moments in the music. To provide an indication of metrical activity that would not focus solely on tempo, we introduce a new measure, called _dynamic metrical centroid_, which takes into consideration the rhythmical activity at the various metrical levels. The overall structure of the proposed method is schematized in Figure 1.  
This approach, compared to all the other participants to the Music Information Retrieval Evaluation eXchange (MIREX) Audio Tempo Extraction competition from 2006 to 2018, is the third best one among those that can track tempo variations. While the two best methods are based on machine learning, our method suggests a way to track tempo founded on signal processing and heuristics-based peak tracking.  
We previously briefly presented the principles of the approach [3] and more recently described the method in more details [4]. This paper discusses the state of the art in more details, provides a more detailed and accurate description of the proposed method, and provides an extended bibliography of the MIREX Audio Tempo Extraction competition [5].