esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on an Insulator Defect Detection Method Based on Improved YOLOv5
## 4 Experimental Results and Analysis
### Model Training
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **P (\%)** & **R (\%)** & **mAP** & **F1** & **GFLOPs** \\ \hline SSD & 84.9 & 81.5 & 82.4 & 81.0 & 18.2 M \\ Fast-RCNN & 93.8 & 83.7 & 87.8 & 88.0 & 44.52 M \\ YOLOv3 & 94.2 & 86.3 & 91 & 89.0 & 191.5 M \\ YOLOv4 & 92.1 & 89.6 & 91.3 & 89.0 & 118.77 M \\ YOLOv5s & 95.6 & 90.7 & 92.7 & 90.0 & 15.8 M \\ YOLOX & 93.8 & 90.0 & 92.4 & 91.0 & 26.61 M \\ YOLOv7 & 94.5 & 91.5 & 93.5 & 93.0 & 102.5 M \\ OURS & 94.8 & 91.9 & 93.7 & 93.0 & 15.65 M \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of different detection algorithms. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Grouping** & **Anchor** & **NAM** & **g\({}^{n}\)Conv** & **mAP** & **mAP** \\ \hline  & & & & **Insulator** & **Defect** \\ \hline
1 & \(\surd\) & & & 0.868 & 0.981 & 0.925 \\ \hline
2 & & \(\surd\) & & 0.841 & 0.943 & 0.892 \\ \hline
3 & & & \(\surd\) & 0.832 & 0.951 & 0.892 \\ \hline
4 & \(\surd\) & \(\surd\) & & 0.866 & 0.978 & 0.922 \\ \hline
5 & \(\surd\) & & \(\surd\) & 0.862 & 0.975 & 0.919 \\ \hline
6 & & \(\surd\) & \(\surd\) & 0.839 & 0.878 & 0.859 \\ \hline
7 & \(\surd\) & \(\surd\) & \(\surd\) & 0.888 & 0.985 & 0.937 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of ablation experiments. Figure 9: mAP and loss curve. (**a**) ours-mAP; (**b**) ours-Class-Loss. As can be seen from the table, the average detection accuracy of the algorithm in this paper is higher than that of other mainstream detection algorithms such as SSD, Fast-RCNN, YOLOv3, YOLOv4, YOLOv5, YOLOX, and YOLOv7 by 11.3%, 5.9%, 2.7%, 2.4%, 1%, 1.3%, and 0.2%, respectively. By comparing the experimental analysis, it is found that the improved algorithm proposed in this paper has higher mAP values on the insulator defect data set and the mAP values of each class than other models. As shown in Figure 10, we use three images of arbitrary insulator defects for different algorithm models to make predictions. After comparison, we find that the Fast-RCNN and SSD algorithms have individual cases of missing detection, and the YOLOv3, YOLOv4, and YOLOv5 algorithms are much better compared to the first two algorithms. The detection accuracy has been improved, and the detection speed is faster. Compared to the other algorithms, our improved algorithm can identify and locate the specific location of insulators and defects almost accurately and without difference from the actual detection results. Figure 10: _Cont._
## 2 Materials and Methods
### EC Measurement  
An OC/EC Analyzer(r) (Sunset Laboratory Inc., Tigard, OR, USA) was used to measure atmospheric EC concentrations and showed information about masses of EC1, EC2, EC3, EC4, EC5 and EC6 which are EC fractions at each temperature profiles applied.  
OC and EC were quantified for 1.5 cm\({}^{2}\) punches of the quartz filter samples using a thermo-optical evolved gas analysis under NIOSH 5040 procedure [10; 20]. Carbon deposited on the filter was oxidized to carbon dioxide, and then reduced to methane for detection with a flame ionization detector (FID) [19]. A laser (wavelength 670 nm) was used to monitor transmittance through the filter [19]. The split time was set as the time at which the transmittance returns to its initial value [19]. As shown in Table 1, most volatilized carbon evolves in the He phase while the temperature is heated stepwise to 310 \({}^{\circ}\)C for 80 s, 475 \({}^{\circ}\)C for 80 s, 615 \({}^{\circ}\)C for 80 s and 870 \({}^{\circ}\)C for 110 s, being analyzed into four OC fractions. The 98% He/2% O\({}_{2}\) atmosphere is introduced after the oven cools to 550 \({}^{\circ}\)C, and the oxidized carbon evolves at 550 \({}^{\circ}\)C for 45 s, 625 \({}^{\circ}\)C for 45 s, 700 \({}^{\circ}\)C for 45 s, 775 \({}^{\circ}\)C for 45 s, 850 \({}^{\circ}\)C for 45 s and 870 \({}^{\circ}\)C for 110 s, being analyzed into six EC fractions, EC1, EC2, EC3, EC4, EC5 and EC6, respectively [19]. The carbon that evolves in the oxidizing atmosphere until the transmitted light returns to its initial value is termed as PyC, which is subtracted from the sum of EC fractions to obtain the EC concentration [19]. In other words, EC mass is calculated by the sum of EC1, EC2, EC3, EC4, EC5 and EC6 minus PyC.  
Figure 1: Location of sampling site at KOREATECH Byeongcheon Campus (36.765\({}^{\circ}\) N, 127.280\({}^{\circ}\) E).
# CCAIB: Congestion Control Based on Adaptive Integral Backstepping for Wireless Multi-Router Network
## 2 Congestion Control Model for Wireless Multi-Router Networks
Denote \(x_{i,1}=q_{i}-q_{i,r}\), \(x_{i,2}=W_{i}\), \(u_{i}=p_{i}\), where \(q_{i,r}\) is the desired queue length for router \(i\). The integral term is defined as \(x_{i,0}=\int_{0}^{t}(q_{i}(\tau)-q_{i,r}(\tau))d\tau\). \(\theta_{i,1}=p_{ul,i}\) and \(\theta_{i,2}=p_{dl,i}\). Then, (1) can be rewritten as:  
\[\left\{ \begin{array}{rl}\dot{x}_{i,0}=&x_{i,1}\\ \dot{x}_{i,1}=&f_{i,1}(x_{i,1})+(g_{i,10}(x_{i,1})+g_{i,11}(x_{i,1})\theta_{i,1})x_{i,2}\\ \dot{x}_{i,2}=&f_{i,2}(x_{i,1})+(g_{i,20}(x_{i,1},x_{i,2})+g_{i,21}(x_{i,1},x_ {i,2})\theta_{i,2})u_{i}\end{array} \right. \tag{3}\]  
where \(f_{i,1}=-C_{i}-\dot{q}_{i,r}\), \(f_{i,2}=\frac{1}{R_{i}}\), \(g_{i,10}=\frac{1}{R_{i}}\), \(g_{i,11}=-\frac{1}{R_{i}}\), \(g_{i,20}=-\frac{1}{R_{i}}-\frac{W_{i}}{2}\frac{W_{i}}{R_{i}}\), and \(g_{i,21}=\frac{W_{i}}{R_{i}}\Big{(}1-\frac{W_{i}}{2}\Big{)}\). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbols** & **Meanings** & **Ranges** \\ \hline \(W_{i}(t)\) & Window size & \([W_{min},W_{max}]\) \\ \(q_{i}(t)\) & Queue length & \([q_{min},q_{max}]\) \\ \(R_{i}(t)\) & RTT & \(\mathbb{R}_{+}\) \\ \(p_{i}(t)\) & Queue packet loss ratio & \([0,1]\) \\ \(C_{i}(t)\) & Link capacity & \(\mathbb{R}_{+}\) \\ \(T_{p}\) & Propagation time & \(\mathbb{R}_{+}\) \\ \(M\) & Number of congestion routers & \(\mathbb{N}\) \\ \(p_{dl,i}(t)\) & Packet loss ratio of downlink & \([0,1]\) \\  & before router \(i\) & \([0,1]\) \\ \(p_{ul,i}(t)\) & Packet loss ratio of uplink & \([0,1]\) \\  & before router \(i\) & \([0,1]\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Meanings and ranges of symbols. Based on (3), the congestion controller based on adaptive integral backstepping is designed in the following section.
# Ammonia Gas Adsorption in Fixed Bed and Fluidized Bed Using Bentonite Particles
## 3 Results and Discussion  
### Sodium Bentonite Characterization  
#### 3.1.1 Granulometric Distribution  
The granulometric distribution for sodium bentonite was obtained for fine particles. The particle size distribution for sodium bentonite was obtained for particles that pass through the sieve (undersize) and for particles that remain on the sieve (oversize). Table 1 presents the mean diameter values for the sodium bentonite depending on length, length weighted, surface, surface weighted, volume and volume weighted. It can be observed that the mean diameter depending on the length, D[1,0], shows a particle size of 11.74 \(\upmu\)m (this value represents the frequently encountered, average size); the standard deviation (STD) has a value of 1.40 \(\upmu\)m and the confidence level (Conf.) is 98.11%.  
Percentiles like D10, D50 and D90 can be obtained directly from Table 1 depending on the number of particles, length, surface and volume. The granulometric distribution on sodium bentonite by volume presents values for percentile D10; 10% of the number of particles (100%) have particle size under 10.30 \(\upmu\)m; for percentile D50, 50% of the number of particles (100%) have particle size under 12.08 \(\upmu\)m; for percentile D90, 90% of the number of particles (100%) have particle size under 14.46 \(\upmu\)m.  
Figure 2 shows the volume histogram and cumulative oversize for sodium bentonite with a mean diameter of 12.28 \(\upmu\)m, standard deviation (STD) of 1.57 \(\upmu\)m and confidence level (Conf.) of 47.41%.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Mean Diameter by:** & **Size (\(\upmu\)m)** & **STD (\(\upmu\)m)** & **Conf. (\%)** & **Dimension** & **D10 (\(\upmu\)m)** & **D50 (\(\upmu\)m)** & **D90 (\(\upmu\)m)** \\ \hline Length, D[1,0] & 11.74 & 1.40 & 98.11 & Number & 10.30 & 11.49 & 13.87 & 10.30 \\ \hline Length weighted, D[2,1] & 11.91 & 1.47 & 98.10 & Length & 10.30 & 11.49 & 13.87 & 10.30 \\ \hline Surface, D[2,0] & 11.83 & - & - & - & - & - & - & - \\ \hline Surface weighted, D[3,2] & 12.09 & 1.52 & 47.43 & Surface & 10.30 & 12.08 & 13.87 & 10.30 \\ \hline Volume, D[3,0] & 11.91 & - & - & - & - & - & - & - \\ \hline Volume weighted, D[4,3] & 12.28 & 1.57 & 47.41 & Volume & 10.30 & 12.08 & 14.46 & 10.30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical data obtained from the granulometric distribution for sodium bentonite.  
Figure 2: Volume histogram and cumulative oversize of sodium bentonite with D10: 10.30 \(\upmu\)m; D50: 12.08 \(\upmu\)m; D90: 14.46 \(\upmu\)m.  
Figure 2 presents a single granulometric distribution class that corresponds to the range of 10-28 \(\upmu\)m with 0-82%. The particle size taken in the analysis of the sodium bentonite (raw material for aluminum pillared bentonite) is 12.08 \(\upmu\)m and can be easily agglomerated with water to form particles of larger diameters.
## 3 Network Model  
A wireless sensor network is composed of a base station and large number of sensor nodes. The network can be represented as a digraph (or directed graph) \(G\). The graph \(G\) is defined as follows:  
\[\begin{split} G=&\left(V,E\right)\\ \text{where,}\\ V=&\left\{v_{1},v_{2},\ldots,v_{n}\right\}\\ E=&\left\{e_{1},e_{2},\ldots,e_{m}\right\}\\ E\subset& V\times V\end{split} \tag{1}\]  
In Equation (1), \(V\) is a set of vertices and each vertex denotes a sensor node. \(E\) is a set of edges and each edge denotes a link between vertices (_i.e._, sensor nodes). For two arbitrary integers \(i\) and \(j\), where \(i\) and \(j\) are less than \(n\), \(e_{ij}\) (\(\in\)\(E\)) indicates a communication link between vertex \(v_{i}\) and \(v_{j}\) (\(v_{i}\),\(v_{j}\in E\)). An in-degree (and out-degree) is the number of inward (and outward) graph edges from a given graph vertex in the directed graph. Figure 1 shows the in-degree and out-degree.  
In the figure, the in-degree and out-degree of \(v_{0}\) are 3 and 1, respectively. We denote that \(v_{0}\) is the super-node for nodes \(v_{1}\), \(v_{2}\) and \(v_{3}\). Also, nodes \(v_{1}\), \(v_{2}\) and \(v_{3}\) are sub-nodes of \(v_{0}\), respectively. Additionally, the number of the in-degree can be represented as an amount of communications.  
In this paper, we propose a path renewal method to uniformly consume energy resources. In our proposal, each sensor node can know the amount of communications and remaining energy. In the figure, if the remaining energy of \(v_{0}\) is less than a threshold value, one of the sub-nodes searches a new super-node. Our proposal is briefly illustrated in the next section.
On the other hand, impedance and admittance control consists of the regulation of mass, stiffness and damping values associated with the overall mechanical impedance of the system under analysis. Impedance control focuses on trajectory tracking, whereas admittance control is based on a force reference. In addition to the aforementioned techniques, more complex control schemes have been developed and can be found in the literature [37; 38]. Control architectures can rely on traditional linear controllers as well as on more refined control strategies as predictive control or model-based robust control [39; 40]. Although the latter accounts for any non-linearity in the system, thus theoretically providing improved performance with respect to simpler linear controllers, a more complex formulation of the control logic can be particularly demanding, and the efficiency of the result strictly depends on the accuracy of the modelling of the plant. Moreover, these techniques often rely on additional data relating to the state of the system,thus requiring a larger number of transducers as well as the development of methodologies aimed at the estimation of unknown quantities that cannot be easily measured [41]. This paper describes a new test-bench conceived for the development of an automatic perturbator aimed at providing a pre-set impulsive contact force to a subject, for clinical evaluation of his postural control ability. The perturbator is based on a linear pneumatic actuator controlled by means of two flow proportional valves. The contact force which originates from the interaction between the perturbator and the subject must be regulated with very demanding features in terms of high dynamics and accuracy, since it must interact with the neuro-sensory apparatus of the subject. An analytical model of the human-machine interface, which is described in detail, supports the Hardware-In-the-Loop (HIL) design. The behavior of the model was compared with experimental data collected from the test-bench. The objective of this work is to show that the proposed HIL system enables an extensive design of the automatic perturbator by appropriate selection of pneumatic components and sensors with the final aim to optimize the performance of the system in terms of force control accuracy. Moreover, the automatic perturbator was tested in a realistic environment, thanks to the ability of the HIL equipment to reproduce the main conditions of a typical dynamic posturographic trial.
# A Neural-Network-Based Watermarking Method Approximating JPEG Quantization
## 4 Proposed Method
### Training Method  
The embedding and extraction networks are trained in the same way as they were in ReDMark [11], respectively. The loss function \(L_{1}\) of the embedding network is defined as  
\[L_{1}=1-\text{SSIM}(\mathbf{I},\mathbf{I}_{o}), \tag{16}\]  
where SSIM represents the structural similarity function (SSIM) [18], which measures the structural similarity between two images. The closer it is to 1.0, the larger the similarity between the two images [18]. It is defined in  
\[\text{SSIM}(\mathbf{I},\mathbf{I}_{o})=\frac{(2\mu\mu_{o}+c_{1})(2\text{Cov}(\mathbf{I}, \mathbf{I}_{o})+c_{2})}{(\mu^{2}\mu_{o}^{2}+c_{1})(\sigma^{2}\sigma_{o}^{2}+c_{2} )}, \tag{17}\]  
where \(\mathbf{I}\) is the original image given as a teacher and where \(\mathbf{I}_{o}\) is the output of the embedding network. \(\mu\) and \(\mu_{o}\) are the means of \(\mathbf{I}\) and \(\mathbf{I}_{o}\), respectively. \(\sigma\) and \(\sigma_{o}\) are the variances of each image, and \(\text{Cov}(\mathbf{I},\mathbf{I}_{o})\) represents the covariance between the two images. Let \(c_{1}\) and \(c_{2}\) be constants, and let \(c_{1}=10^{-4},c_{2}=9\times 10^{-4}\). Because the output of the embedding network takes a real number, the stego-image \(\mathbf{I}_{\text{st}}\) is obtained by converting it back to 256 levels of the pixel value. That is, it is given by  
\[I_{\text{st}}(i,j)=\begin{cases}255,&I_{\text{o}}(i,j)>0.5\\ \lfloor 255\ I_{o}(i,j)+128\rfloor.&-0.5\leq I_{\text{o}}(i,j)\leq 0.5.\\ 0,&I_{\text{o}}(i,j)<-0.5\end{cases} \tag{18}\]  
The loss function \(L_{2}\) of the extraction network is defined as  
\[L_{2} = -\sum_{i=0}^{h-1}\sum_{j=0}^{w-1}\{p(i,j)\log(p_{o}(i,j))+(1-p(i, j))\log(1-p_{o}(i,j))\}, \tag{19}\]  
where \(\mathbf{p}\) is the watermark used as a teacher for the extraction network. \(\mathbf{p}_{o}\) is the output of the sigmoid function of the extraction network. That is, the value of the element takes a value between 0 and 1. The total loss function \(L\) of our network is defined as  
Figure 8: Proposed attack layer.  
\[L=\gamma L_{1}+(1-\gamma)L_{2}, \tag{20}\]  
where the parameter \(\gamma\) determines the balance between the two loss functions. The embedding and extraction network are trained by the back propagation [19] using stochastic gradient descent (SGD) as the optimization method.
_Article_  
**Interaction between Sound and Thermal Influences**  
**on Patient Comfort in the Hospitals of China's Northern Heating Region**  
**[PERSON], [PERSON] *, [PERSON] and [PERSON]**  
Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology,  
Ministry of Industry and Information Technology, School of Architecture, Harbin Institute of Technology,  
Harbin 150001, China; [EMAIL_ADDRESS] (Y.W.); [EMAIL_ADDRESS] (L.L.); [EMAIL_ADDRESS] (J.M.)  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +86-115104515125  
Received: 10 November 2019; Accepted: 11 December 2019; Published: 17 December 2019  
**Featured Application: Authors are encouraged to provide a concise description of the specific application or a potential application of the work. This section is not mandatory.**  
**Abstract:** Previous studies have found that hospitals are often inadequately ventilated in the heating region of China, which causes an increased risk of negative impacts on patients. The complex interaction between thermal comfort and acoustics presents considerable challenges for designers. There is a wide range of literature covering the area of the interaction between the sound-thermal, sound-odor, and acoustic-visual influences, but a focused research on the sound -thermal influence on comfort in hospitals has not been published yet. This paper describes a series of field measurements and subjective evaluations that investigate the thermal comfort and acoustic performance of eighteen hospitals in China. The results showed that the thermal comfort in the monitored wards was mostly acceptable, but the temperatures tended to be much higher and the humidity much lower, in practice than they were designed to be in the heating season. The most significant conclusion is that a positive thermal stimulus can create a comfortable thermal environment, which can improve patients' evaluation of the acoustics, while a negative stimulus has the opposite effect. A comfortable acoustic environment also caused patients to positively evaluate thermal comfort. Moreover, the relationship between thermal and sound effects in the overall evaluation showed that they are almost equal.  
**Keywords:** patient satisfaction; thermal comfort; acoustic comfort; heating region; hospitals
## 3 Experimental Method and Results
### Results of the Selected Wavelengths  
The clustering method was used to select the excitation wavelengths for each pure compound with the pure compound EEM. The tolerant criterion was set to \(1.3\times 10^{-3}\). Table 3 gives the 10 selected excitation wavelengths of each component from 41 excitation wavelengths. The selected excitation wavelengths of each component are different, which can presume it relates to their specific molecular structures.  
We used the emission spectra corresponding to the 10 selected excitation wavelengths of the nine training samples to find the emission wavelengths based on the method given in Section 2.2. The dots in Figure 3a,c,e are the selected emission wavelengths, which are 63 for naphthalene, 64 for 1-naphthhol, and 71 for 2-naphthhol. These emission wavelengths fell within the emission spectra peak band of each compound excited by specific wavelength, as plotted in Figure 4. For comparison, we used the same emission spectra corresponding to the selected excitation wavelengths to select the emission wavelengths by the GA method, as shown in Figure 3b,d,f. As it can be seen in Figure 3b,d,f, these wavelengths extended across the entire band of the emission wavelengths. The Monte Carlo method in this study is more effective than the GA method presumably that the emission wavelengths within the peak band contribute most to the concentration prediction.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Name of the Pure Compound** & \multicolumn{8}{c}{**Selected Excitation Wavelengths**} \\ \hline naphthalene & 220 & 224 & 230 & 234 & 236 & 252 & 254 & 262 & 270 & 272 \\
1-naphthol & 222 & 230 & 248 & 250 & 252 & 256 & 258 & 260 & 290 & 292 \\
2-naphthol & 248 & 250 & 252 & 254 & 260 & 262 & 266 & 268 & 278 & 288 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The selected excitation wavelengths for the pure compound (nm).  
Figure 3: Selected emission wavelengths of the three components. The dots in (**a**,**c**,**e**) are the selected wavelengths by the Monte Carlo method. The dots in (**b**,**d**,**f**) are the selected wavelengths by the GA (genetic algorithm) method.
# Numerical Study of the Seismic Response of an Instrumented Building with Underground Stories
## 3 Numerical Model
### Numerical Models vs. Recorded Measurements Comparisons
#### 4.2.1 Comparison between the Foundation and First-Floor Accelerations  
Accelerations in each direction were compared between the first floor and foundation slab for each event in the data set. Accelerometers 1 (foundation) and 5 (ground floor) were used for the X-direction; 2 (foundation) and 6 and 7 (average, ground floor), for the Y. Figure 6 shows this comparison in a plot where the position of the blue dots graph the foundation vs. first-floor accelerations. The red line is a linear regression as determined by the equation in the upper left corner. The yellow line, 45\({}^{\circ}\), corresponds to perfect data coincidence.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Event \#** & **Total Record Time (s)** & **Zero Time (s)** & **Time Window (s)** \\ \hline
5 & 231.99 & 27.0 & 56.00 \\
12 & 248.99 & 27.0 & 56.00 \\
37 & 264.99 & 20.0 & 112.01 \\
47 & 383.99 & 29.0 & 168.02 \\
52 & 320.99 & 45.9 & 56.00 \\
55 & 406.99 & 31.0 & 112.01 \\
57 & 344.99 & 5.0 & 112.01 \\
62 & 258.99 & 0.0 & 56.00 \\
65 & 322.99 & 11.0 & 56.00 \\
71 & 278.99 & 11.5 & 56.00 \\
72 & 295.99 & 12.0 & 56.00 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Time window duration for each analyzed event.  
Figure 6: Seismic acceleration \(x\)-axis, foundation vs. first floor (10/22/2010).  
For most of the analyzed events, although there is a slight similarity in point-to-point accelerations between the foundation and first-floor data, the graphed slope varies by more than 10%. A slope of 1 would imply that both records were the same, and therefore, the response of the entire underground structure can be assumed to be similar to that of a rigid body. These results suggest that the first level and the foundation do not move exactly in the same way: indeed, the slopes indicate that accelerations are at least 10% higher, on average, for the first floor than in the foundation, as it can be observed in Figure 6.