esa-sceva/satcom-chunk-collection huggingface dataset:
# A Reliable Scheme for Synchronizing Multimedia Data Streams under Multicasting Environment
## 4 Proposed Solution
### Proposed Design Requirements
#### 4.2.7 Playout Delay Offset  
Playout-delay offset refers to the time taken for a packet to be transmitted from sender till it is presented to the playout device. It includes network delay, machine processing delay and the queuing delay at receiver.  
Sender takes system time (sent time of SR packet) and adds playout delay offset to it to generate playout time as:  
\[t_{p}=t_{s}-d_{\mathit{off}} \tag{4}\]  
Delay offset value remains same for all packets and receivers. The only sender is authorized to change this value if desired. Here one thing is very important to understand; RTCP packets are same for all receivers thus all receivers will receive the same value of system time (sent time of SR packet) and Playout time contained in sender's APP packet.  
Figure 7: A timeline overview of the solution.
## 4 Discussion  
### Mangrove Classification and Loss  
SVM classification has been widely explored on different types of remote sensing data and at global, regional, and local scales with high accuracy [64]. [PERSON] et al. [65] reviewed land cover classification using different techniques and concluded that SVM is superior compared to other classification techniques. Classification works related to mangrove area identification and mapping using SVM classifiers showed high accuracy [41; 66; 67]. SVM proved to be one of the best classifiers where high accuracy (~90%) was achieved based on the Landsat 7 ETM+ pan-sharpened image and the Sentinel-2A image. While several global mangrove maps are available at 30 m [34; 53; 68; 69], the use of higher spatial resolution satellite images (15 m and 10 m) in this study aimed to increase the accuracy of mangrove extent mapping. The classification result showed clear delineation between mangrove, water bodies, and other land use and land cover, providing high quality mangrove cover in 2000 and 2019 for mangrove change identification. In a previous study, [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [32] performed land use classification of the IM region from 1989 to 2014 using multiple Landsat images. They showed that, during the 21 st century, urban area significantly increased, converting from oil palm, rubber, mangrove, and forest.  
The spatial pattern of mangrove changes in IM shows a significant loss in the southwestern part, where the three Ramsar sites are located (Figure 3c). The Ramsar sites have been protected since 2003; thus, the loss may be due to a high input of sediments generated via construction and land reclamation activities around the Sungai Pulai estuary [70], where the development of the Tanjung Bin Power Plant, the Forest City, and the expansion area of the Tanjung Peleps Port (Figure 1) are located. Reports including the Marine Environment Protection Committee (MEPC) [71] stated that the increasing traffic of ships and vessels in this region generates wave currents that can destroy mangroves in coastal areas [72].  
Within the mangrove region of IM, 40 sites with aquaculture activities were detected from the satellite images. Aquaculture conversion from mangrove forests is very popular, as can be seen in Indonesia, where there has been a 40% mangrove loss over the past three decades [73]. Figure 4b shows the locations of these aquaculture sites. Most of the aquaculture sites are located on the \"others\" land use, at the boundary of the mangrove area. Thus, we can conclude that aquaculture activities contributed greatly to the loss of mangrove areas during the study period. At the eastern side of IM (Figure 4b), more aquaculture sites were found compared to the western site (where the Ramsar sites are located). One of the reasons for this could be that the Ramsar sites are protected from these conversion activities while, on the eastern side, where the Johor River flows, the accessibility to water from the Johor River and the richness of nutrients at the site have encouraged the conversion of mangrove areas to aquaculture use.
# AIR-YOLOv3: Aerial Infrared Pedestrian Detection via an Improved YOLOv3 with Network Pruning
## 4 Experiment
### Ablation Experiment
#### 4.4.2 Channel Pruning  
After the sparse training, the value of the \(\gamma\) is pushed toward zero. The pruning ratio determines the number of pruned channels. \(\eta\) is the channel pruning ratio and is defined as  
\[\eta=TP/TC\times 100\% \tag{4}\]  
where \(TP\) represents the number of pruned channels, and \(TC\) is the number of sparse model channels.  
In order to find the optimal pruning ratio, we set different pruning ratios in the experiments. The trend of the model's AP, inference time, model volume, and the number of remaining channels are shown in Figure 6.  
In the experiment, we set the \(\eta\) to 0%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 90%, 90%, 97%, 98%, and 99% to compare the model's AP, inference time, model volume, and the number of pruned channels, respectively. Figure 6a shows that the model's AP declined with the channel pruning ratio. Before the pruning ratio is set to 90%, the AP decreases slowly, basically remaining above 92%. When the prun  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**k** & **Accuracy** (\%) & **Anchors** \\ \hline
6 & 78.27 & (13,21), (21,49), (30,68), \\  & & (44,101), (9,20), (16,34) \\
7 & 79.47 & (25,66), (16,34), (11,22) \\  & & (20,48), (53,113), (32,57), (36,83) \\
8 & 79.86 & (44,101), (21,51), (19,20), (11,24) \\  & & (9,19), (30,68), (17,36), (14,30) \\
9 & 81.31 & (16,35), (54,114), (38,86), (31,65) \\  & & (20,46), (13,28), (19,20), (10,21), (23,59) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The accuracy analysis with different \(K\) values.  
Figure 6: Influence of channel prune ratio: (**a**) AP curve, (**b**) inference time, (**c**) model size and (**d**) the number of channels.  
the AP decreases rapidly. Figure 6b shows that the inference time fluctuates around 27 ms, regardless of the channel pruning ratio. Relatively speaking, the depth of the model has a greater effect on inference time [30].  
From Figure 6c, it is obvious that with the channel pruning ratio increased, model size basically linearly declines.  
Figure 6d is a bar chart of the number of remaining channels with different channel pruning ratios. Before the pruning ratio is set as 90%, the remaining channel is basically equal to the total number of channels (25,920) multiplied by the pruning ratio. After that, the channel number had little change due to the limitation of the local threshold \(\pi\), which is the minimum value within the maximum \(\gamma\) of every BN layer.  
Through the above experiments, it can be determined that when the channel pruning ratio is about 90%, we can obtain a large compression rate with a small decrease in accuracy. Specifically, the compressed model size is 27.1 MB, and the size of the original model is 238.9. The model size decreased by 88.6%, while the accuracy is just reduced by 0.9%.
## 3 Experimental Study
### Experimental Process  
During the experimental process, tap water was introduced into the system for both heat storage and heat release operations. Throughout the heat storage phase, hot water flowed into the upper portion of the storage tank, whereas cold water entered the lower section. After the heat pump was deactivated, stratification occurred in the water stored in the tank. Slight changes were observed in both the tap water pressure and tank inlet velocity during the heat release phase. In addition, the inlet water temperature underwent temporal variations owing to fluctuations in the outdoor temperature.  
The thermal storage tank was designed according to the manufacturer's specifications, with specific parameters for its internal lining: a diameter (D) of 42 cm and height (H) of 142 cm, giving a height-to-diameter ratio between 3 and 4, which was optimized to maintain excellent stratification [30; 31; 32]. It had a capacity of 200 L, and the water inlet was positioned 20 cm from the bottom. In addition, the outlet was 20 cm from the top, utilizing bent-pipe configurations for both the entry and exit. A physical drawing of the tank is shown in Figure 5, and a schematic of the internal structure is shown in the physical model below, which is also equipped with a drain and negative pressure valve. To ensure continuous observation of the tank layering phenomenon, six temperature measurement points, labeled points 1 through 6 in descending order from the top to the bottom of the tank, were set up. The measurement points are counted from top to bottom as points 1-6. These points were connected to a control cabinet, which displayed the temperature of each measurement point to monitor the stratification of the water in the tank. A diagram of the flow meter and temperature sensor is shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Equipment** & **Manufacturer** & **Model Number** & **Range** & **Accuracy** \\ \hline Flowmeter & Anhui Ruiliang Measuring & LWGY-FMT- & 0.8 m\({}^{3}\)/h to 8 m\({}^{3}\)/h & \(\pm\)0.004 m\({}^{3}\)/h \\  & Anhui, China & WZPT-035-GK- & \(-\)50 \({}^{\circ}\)C to 200 \({}^{\circ}\)C & \(\pm\)0.1 \({}^{\circ}\)C \\ Temperature sensor & Shenzhen, China & & F3 PF & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the experimental equipment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Equipment** & **Heat Pump** & **Circulation Pump** \\ \hline Manufacturer & McQuay & ZHONGKE CENTURY \\ Model number & MWW040 ARS & DC55B-24160 PWM \\ Rated power & 3.1 kW & 80 W \\ Maximum head & – & 16 m \\ Maximum flow & 2.26 m\({}^{3}\)/h & 2.4 m\({}^{3}\)/h \\ Rated voltage & 380 V & 24 V \\ Rated frequency & 50 Hz & – \\ \hline \hline \end{tabular}
\end{table}
Table 2: Introduction to the basic parameters of experimental equipment.
## 2 Materials and Methods
### Data Collection
#### 2.2.2 Hydrometric Data Collection  
A meteorological station was installed at each site that included a net radiometer (3.75 m above ground; CNR4, Kipp & Zonen, Delft, The Netherlands), two heat flux plates (5 cm below ground; HFP01, Hukseflux Thermal Sensors, Delft, The Netherlands), four ground temperature probes (108 thermistors, Li-Cor Inc., Lincoln, NE, USA) at 5, 10, 25 and 50 cm depths, and a temperature and relative humidity probe (HMP 155, 2.5 m above ground; Vaisala Oyj, Vantaa, Finland) where all data was recorded every 30 min to the data logger (XLite 9210B, Sutton Corporation, Stirling, Loudoun County, VA, USA). Additional temperature and relative humidity sensors (HOBO U23 Pro v2, Onset Hobo, Bourne, MA, USA) were installed at 1 and 3.5 m above ground level for quality control. Precipitation was measured using a tipping bucket rain gauge (RG3, Onset Hobo, Bourne, MA, USA) installed 1.5 m above ground. An eddy covariance (EC) system, which included a closed-path infrared gas analyzer (4 m above ground surface; LI 7200, Li-Cor Inc., Lincoln, NE, USA) paired with a three-dimensional ultrasonic wind anemometer (4 m above ground; Windmaster Pro, Gill Instruments, Lymington, County of Hampshire, UK) sampling at a rate of 20 Hz, was installed on the same tower as the meteorological tower. The sensor heights were >3 m above maximum canopy of alfalfa and >1 m above maximum canopy of maize, where canopy dependent calculations and flux footprints varied depending on measured canopy height throughout the season.
###### Abstract  
Services from the Continuously Operating Reference Stations (CORS) of the Global Navigation Satellite System (GNSS) provide data and insights to a range of research areas such as physical sciences, engineering, earth and planetary sciences, computer science, and environmental science. Even though these fields are varied, they are all linked through the GNSS operational application. GNSS CORS have historically been deployed for three-dimensional positioning but also for the establishment of local and global reference systems and the measurement of ionospheric and tropospheric errors. In addition to these studies, CORS is uncovering new, emerging scientific applications. These include real-time monitoring of land subsidence via network real-time kinematics (NRTK) or precise point positioning (PPP), structural health monitoring (SHM), earthquake and volcanology monitoring, GNSS reflectometry (GNSS-R) for mapping soil moisture content, precision farming with affordable receivers, and zenith total delay to aid hydrology and meteorology. The flexibility of CORS infrastructure and services has paved the way for new research areas. The aim of this study is to present a curated selection of scientific papers on prevalent topics such as network monitoring, reference frames, and structure monitoring (like dams), along with an evaluation of CORS performance. Concurrently, it reports on the scientific endeavours undertaken by the Geomatics Research Group at the University of Palermo in the realm of GNSS CORS over the past 15 years.  
GNSS; CORS; network; structures monitoring; accuracy
## 2 Results
### Analysis of the \(\beta\)-Fructofuranosidase Activity Expressed in P. pastoris
Maximum production of 6-kestose, 53.2 g/L, was reached after 7 h and represented \(\sim\)59% (w/w) of the total FOS produced (Figure 4c). At that point, 12.2 g/L, 9.5 g/L, 2 g/L, and 1.9 g/L of neokestose, blastose, 1-kestose, and neonystose, respectively, were obtained. \begin{table}
\begin{tabular}{c c c} \hline \hline  & \(\beta\)-Fructofuranosidase (U/mL) & \\ \hline
**Substrate** & _P. pastoris_ & _R. dairenensis_ \\ \hline Sucrose & 20.5 \(\pm\) 1.4 & 12.4 \(\pm\) 1.1 \\
1-Kestose & 4.1 \(\pm\) 0.03 & 1.8 \(\pm\) 0.02 \\ Nystose & 0.4 \(\pm\) 0.04 & 0.4 \(\pm\) 0.02 \\ Raffinose & 16.7 \(\pm\) 0.2 & 11.0 \(\pm\) 0.3 \\ Inulin & 3.2 \(\pm\) 0.05 & 2.8 \(\pm\) 0.03 \\ \hline \hline \end{tabular} Values are the average of three measures \(\pm\) standard errors.  
\end{table} 
Table 1: Substrate specificity of RdINV expressed in the indicated yeast. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Substrate** & \(K_{\text{m}}\) & \(k_{\text{cat}}\) & \(k_{\text{cat}}/K_{\text{m}}\) \\  & **(mM)** & **(l/s)** & **(l/s mM)** \\ \hline _P. pastoris_ & Sucrose & 6.2 \(\pm\) 1.0 & 2236 \(\pm\) 356 & \(360\pm 57\) \\  & 1-Kestose & 56 \(\pm\) 5 & 613 \(\pm\) 49 & \(11\pm 1\) \\ _R. dairenensis_ & Sucrose & 6.4 \(\pm\) 0.9 & 4458 \(\pm\) 628 & 698 \(\pm\) 98 \\  & 1-Kestose & 57 \(\pm\) 4 & 1753 \(\pm\) 136 & \(30\pm 2\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Kinetic parameters of RdINV expressed in the indicated yeast. Reaction rate measurements were performed in triplicate. Values of \(k_{cat}\) were calculated considering a protein molecular mass of 170 kDa. The \(k_{cat}/K_{m}\) standard errors were obtained by fitting the normalized Michaelis-Menten equation \(\upsilon=(k_{cat}/K_{m})+[S]/(1+[S]/K_{m})\). Potential of RdINV to synthesize new fructosylated products was explored using different hydroxylated acceptors alternative to sucrose in the transfructsoylating reactions (Table 3). Chromatographic analyses of the reactions showed that two of the six monosaccharides assayed, glucose and fructose, significantly increased the blastose signal or generated two new peaks, respectively, which would be compatible with their fructosylation. Furthermore, six of the seven disaccharides and four of the six aliditols assayed generated new peaks, which were absent in control reactions. Figure S2 shows some representative chromatograms of reactions including positive acceptors.
# A Fire Detection Algorithm Based on Tchebichef Moment Invariants and PSO-SVM
## 3 Feature Extraction Based on TMIs  
Translation, rotation, and scaling are the important transformations of spatial coordinates. In order to guarantee that the fire images can be recognized correctly and steadily, regardless of their position and orientation in the scene and the fire-to-camera distance, the Tchebichef moment invariants with respect to TRS are proposed to extract the features of images in this paper.
## 5 Proposed Classification
### Classification of RL-Based Routing Methods with Regard to Learning Algorithm  
According to the proposed classification, RL-based routing schemes are divided into traditional RL-based and DRL-based in accordance with the learning algorithm. Table 5 compares the strengths and weaknesses of RL and DRL-based routing protocols.  
* **Traditional RL-based routing schemes:** These approaches allow the learning agent to learn the network environment without any information about it to get the most suitable path. Figure 7 displays the traditional reinforcement learning system. These routing methods have a good performance and can find the best route at an acceptable time when the state and action sets are small. However, VANETs are usually large-scale and the state space and the action space are large. Under these conditions, these route protocols have a low convergence speed and require more time to obtain the best response [40; 41].
* **DRL-based routing methods:** They use deep learning (DL) to improve the learning rate. DRL integrates deep learning and reinforcement learning (RL). Figure 8 shows a DRL system. It is an improved version of RL that can solve complex computational processes. In complex environments such as VANETs, it is very complex to estimate the value function and the policy [38; 42]. For this reason, a deep network is used to approximate these values. It accelerates the learning ability of the agent to optimize this policy. These routing protocols have a good learning rate and are an appropriate option for large-scale networks [40; 43].  
Figure 8: Deep reinforcement learning system.  
Figure 7: Traditional reinforcement learning system.
# Multiple Feature Integration for Classification of Thoracic Disease in Chest Radiography
## 2 Related Works
Their proposed model was shown to outperform three previously state-of-the-art deep learning models in ChestX-ray14 by using the official patient-wise split without extra training data. A unified model that jointly employed disease identification and localization with the limited localized annotations from the ChestX-ray14 dataset was proposed and good results were achieved [37]. The text-image embedding network (TieNet) was proposed for text representations of distinctive image extraction [45]. The authors first used TieNet to classify ChestX-ray14 images based on both the image features and the texts received from corresponded reports. Later, TieNet was transformed into a CXR reporting system as a simulation that could output disease classification and a preliminary report. Meanwhile, the authors provided a comparison of different deep learning model settings (ResNet-38 and ResNet-101) in [46]. They achieved the best overall results with the optimized ResNet-38-large-meta architecture trained with CXRs and incorporated non-image dataset (i.e., view positions, age, and gender). From the existing reports on CXR, we can conclude that transferring the features extracted from pretrained networks is preferable. However, the use of shallow handcrafted features from CXR images or the integration of those conventional features and transferred deep features extracted from CNNs has not been considered. To the best of our knowledge, no method has been applied on natural images for either combining different shallow features [47] or integrating different layers of features from pretrained CNNs [48] that resulted in much higher classification accuracy. Accordingly, in this work, we attempt the multiple feature integration of both shallow handcrafted and deep features for ChestX-ray14 images classification.
## 3 Compound Controller Design
### Stability Analysis
 \tag{46}\]  
Until now, we have proved that \(-\sigma\leq s(k+1)\leq\sigma\) with \(s(k)\geq 0\). _Case 2:_ The proof of \(-\sigma\leq s(k+1)\leq\sigma\) with \(s(k)<0\). When \(s(k)<0\), Equation (41) becomes  
\[s(k+1)=-(1-\kappa_{1}\xi_{1})\varsigma\psi(\beta)\Phi+\kappa_{1}\xi_{2}[ \varsigma\psi(\beta)\Phi]^{\beta}sgn(\varsigma\psi(\beta)\Phi)+\kappa_{1} \mathbf{C}\left[\mathbf{D}(k)-\mathbf{\dot{D}}(k)\right] \tag{47}\]  
If \(-1<s(k)<0\), by Proposition 1 and Assumption 3, it can be derived from Equation (47) as  
\[ \begin{split} s(k+1)&\leq-(1-\kappa_{1}\xi_{1}) \varsigma\psi(\beta)\Phi+[\varsigma\psi(\beta)]^{\beta}(1-\kappa_{1}\xi_{1}) \Phi+\varepsilon\\ &\leq-(1-\kappa_{1}\xi_{1})\varsigma\psi(\beta)\Phi+[\varsigma \psi(\beta)]^{\beta}(1-\kappa_{1}\xi_{1})\Phi+(1-\kappa_{1}\xi_{1})\Phi\\ &\leq(1-\kappa_{1}\xi_{1})\Phi\psi(\beta)\\ &\leq\psi(\beta)\Phi=\sigma\end{split}  \tag{48}\]  
Otherwise, when \(s(k)\leq-1\), utilizing Proposition 1 and Assumption 3, Equation (47) can be deduced as  
\[ \begin{split} s(k+1)&\leq-(1-\kappa_{1}\xi_{1}) \Phi\left[\varsigma\psi(\beta)+[\varsigma\psi(\beta)]^{\beta}\right]+ \varepsilon\\ &\leq\varepsilon\leq(1-\kappa_{1}\xi_{1})\Phi\leq\psi(\beta) \Phi=\sigma\end{split}  \tag{49}\]  
Next, the proof of \(-\sigma\leq s(k+1)\) with \(s(k)<0\). Similarly, if \(-1<s(k)<0\), by Proposition 1 and Assumption 3, it can be derived from Equation (47) as  
\[ \begin{split} s(k+1)&\geq-\varsigma\psi(\beta)\[PERSON]+ \kappa_{1}\xi_{1}\varsigma\psi(\beta)\[PERSON]+\kappa_{1}\xi_{2}[\varsigma\psi( \beta)\[PERSON]]^{\beta}-\varepsilon\\ &\geq-\varsigma\psi(\beta)\[PERSON]+\kappa_{1}\xi_{1}\varsigma\psi( \beta)\[PERSON]+\varepsilon[\varsigma\psi(\beta)]^{\beta}-\varepsilon\\ &\geq-(1-\kappa_{1}\xi_{1})\varsigma\psi(\beta)\[PERSON]+\left[[ \varsigma\psi(\beta)]^{\beta}-1\right]\varepsilon\\ &\geq-\epsilon\left[\varsigma\psi(\beta)-[\varsigma\psi(\beta)] ^{\beta}+1\right]\\ &\geq-(1-\kappa_{1}\xi_{1})\[PERSON]>-\sigma\end{split}  \tag{50}\]  
Otherwise, when \(s(k)\leq-1\), utilizing Proposition 1 and Assumption 3, Equation (47) can be deduced as \[s(k+1) \geq -(1-\kappa_{1}\xi_{1})\Phi\left[\varsigma\psi(\beta)-[\varsigma\psi( \beta)]^{\beta}+1\right] \tag{51}\] \[\geq -(1-\kappa_{1}\xi_{1})\psi(\beta)\Phi\] \[\geq -\psi(\beta)\Phi=-\sigma\]  
To this end, we have completed the proof of \(-\sigma\leq s(k+1)\leq\sigma\) with \(s(k)\in\Delta\). That is to say, the sliding mode state \(s(k)\) will enter the region \(\Delta\) within \(K^{*}\) steps and stay in the DTSM domain thereafter by using the proposed DTSM control law.