esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Parameter Settings
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Parameter** & **Description** & **Units** \\ \hline \multirow{4}{*}{Building component extraction} & \(\theta_{v}\) & Angle between alternative building components and alternative wall plane & Degree \\  & \(\theta_{b}\) & Angle between alternative wall surfaces & Degree \\  & \(d\) & Distance between alternative wall surfaces & Meter \\  & \(h_{p}\) & Height difference between the top of alternative wall surfaces & Meter \\ \hline Wall & \(r_{w}\) & Radius of mutual search of boundary wall segments and guide lines & Meter \\  & segment & \(r_{c}\) & Search radius of connection relations of indoor wall segments & Meter \\  & restoration & \(\lambda\) & The balance parameter between the data term and the smoothing term & - \\ \hline \multirow{4}{*}{Indoor space decomposition} & \(n\) & Number of emitted rays & - \\  & \(\varepsilon_{d}\) & Percentage of real wall segments in the boundary of the virtual enclosed area & - \\  & \(\alpha\) & The percentage of the marked area in the free-space evidence raster & - \\ \hline \multirow{4}{*}{Door extraction} & \(h_{max}\) & The maximum limit of door height & Meter \\  & \(h_{min}\) & The bottom limit of door height & Meter \\  & \(w_{max}\) & The maximum limit of door width & Meter \\  & \(w_{min}\) & The minimum limit of door width & Meter \\  & \(P_{col}\) & Filling threshold for column pixels on the wall surface & - \\  & \(P_{row}\) & Filling threshold for row pixels on the wall surface & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of parameters for model extraction and reconstruction. Figure 13: The effect of the change in \(\lambda\) on wall restoration.
# A Compact Wearable Textile Antenna for NB-IoT and ISM Band Patient Tracking Applications
## 2 Antenna Design Methodology  
### Antenna Design  
The proposed multiband wearable antenna, depicted in Figure 2, is a stepped square planar monopole antenna with asymmetric meandering slots on top and backed in a partial and slotted ground plane. The antenna is designed on a denim substrate of permittivity (\(\varepsilon_{r}\)) and loss tangent (tan \(\delta\)) of 1.72 and 0.045, respectively. The Ansys HFSS 3D-electromagnetic tool was used for antenna design. The antenna's total size is 60 mm \(\times\) 60 mm; with detailed dimensions given in Table 2. Here, the lower NB-IoT 1.8 GHz frequency band is achieved by the stepped structure of the top patch. The asymmetric meandering slot on the top patch and slotted ground plane with stubs helped to achieve RFID ISM 2.45 GHz and 5.8 GHz with wideband behavior.  
Figure 1: Wearable textile monopole antenna on phantom model (\(W_{Ph}=L_{Ph}=150\) mm).  
Figure 2: Design of antenna: (**a**) Top view (**b**) Bottom view.
Since consumers prefer ready-to-eat products that are as natural as possible and have sufficient shelf life, it is important to consider appropriate minimal processing without altering the content and bioavailability of major nutrients [4]. In this sense, HHP, a non-thermal technology, seems to be a promising technique for preserving heat-sensitive compounds (for example, aldehydes, pyrazines and furans) and maintaining sensory quality such as colour, as well as inactivating microorganisms and enzymes [5; 6]. HHP (100-800 MPa) has been shown to inactivate a wide range of microorganisms and endogenous enzymes while retaining the sensory properties and nutritional value of food [7; 8]. [PERSON] et al. [9] reported the effect of high-pressure treatment (300 MPa/20 min) on crab meat during storage at 4 \({}^{\circ}\)C, and indicated that the total viable counts (TVC) (5.71 log10 CFU/g) were still below the limit (6 log10 CFU/g) on day 8. [PERSON] et al. [10] found that HHP could reduce the bacterial load and retard TVBN production, in addition, HHP conditions of \(\geq\)400 MPa for 3 min significantly extended the shelf life of hard clams during refrigerated storage. [PERSON] et al.'s [11] results suggested that HHP treatment would be useful for inhibiting the activity of urease, thereby reducing the fishy smells from fish. However, expensive equipment and limited inactivation of spores are major restrictions to the widespread application of HHP [12].  
SV temperature cooking is a food preparation process of heating samples for a long time at a lower temperature [13]. The food is heated to the central temperature of 58-95 \({}^{\circ}\)C in a vacuum sealing system. Compared with traditional cooking methods, this method is characterized by less efflux of soluble nutrients [14]. The sample is vacuum-packed in cling bags before heating, which reduces oxidation and prevents cross contamination. The reported advantages of using SV are the reduction of cooking losses and lipid oxidation while enhancing colour and flavour [15]. In addition, SV can improve meat tenderness [16]. [PERSON] et al. [17] showed that during long-term thermal processing, SV could significantly reduce the level of protein oxidation and fat oxidation, and effectively preserve the flavour and nutrition of braised meat. The cooking yield of poultry meat processed by the SV method (88.5%) was higher than that by the traditional steaming method (71.0%). The meat was found to be redder (a* values of 2.54 and 0.74, respectively) and less yellow (b* values of 15.12 and 16.49, respectively), as well as more tender [18].  
Two emerging technologies, HHP and SV are regarded as effective and economical methods. The utilization of HHP or SV alone to effectively control food spoilage has been widely reported. However, reports are rare exploring the effects of HHP combined with SV on largemouth bass as far as we know. In this study, the effects of HHP and HHP+SV on the quality of largemouth bass were evaluated by measuring the indexes of microorganism, pH value, sensory, colour, texture, GC-MS and FAA.
###### Abstract  
Against the update of load design standards and requirement of long-term service, many latticed towers using steel angles subjected to gradual performance degradation must be retrofitted for bearing further social functions, e.g., electric power transmission and fifth-generation mobile communication. Therefore, this paper proposed a non-destructive reinforcement method for steel angles by avoiding unnecessary new construction or complex reinforcement procedure. The non-destructive reinforcement of a steel angle is composed of a steel angle, reinforcement plate, and fixture. Standardized fixtures are used to connect the reinforcement plate and steel angle to achieve steel angle reinforcement. In retrofitting tests, built-up steel angle members were loaded under axial compression, in which the failure mode and reinforcement effect of key parameters (e.g., clamp type, slenderness ratio, and clamp distance) were analyzed and compared, where a significant reinforcement effect was obtained with the capacity increment within 39-174%; the clamp types and clamp distance had a slight effect on bearing capacity; and the proposed reinforcement method was more effective for slender members. Based on the mechanical mechanism analysis and failure mode, the accuracy of the design method for calculating the bearing capacity of those built-up steel angle members was suggested and verified, in which a simplified mechanical model for the flexural-buckling mode was developed. The design method based on A3C360-16 agreed well with the test result and could be effectively used for calculating the flexural-torsional bearing capacity of those built-up steel angles. This study can provide a valuable reference for the design and application of non-destructive reinforcement of angle steel towers.  
non-destructive reinforcement; built-up steel angles; failure mode; bearing capacity; design method +
Footnote †: journal: Applied Sciences
## 5 WST Applications
### Crop Canopy Influence  
Another factor that changes over time is the density of the leaves in the crop. Signal propagation above the cross canopy results in attenuation and variance in the received signal strength [59, 61]. The density of the leaves in the crop changes over time. When there are less leaves the message rate increases. [PERSON] studied the transmission range of a 2.4 GHz Bluetooth device, finding the optimal distances with radio heights of 1.4 m, 1.7 m and 4 m across bare soil, soybeans and corn respectively [62]. [PERSON] and [PERSON] found that the weakest signals were measured in July, when the total crop canopy was fully developed. Results showed that a dry, full developed crop canopy limits the distance that radio's can cover to around 11 meters when placed near the soil surface. Thus, 100 motes per hectare would be necessary for a reliable communication over the crop canopy [12]. [PERSON] _et al_. showed that attenuation and signal strength variance were dependent on line of sight losses and heights less than the Fresnel zone radius [63]. Experimentation in mature corn fields (2.5 m high) with transceivers placed at antenna heights of 1.5 and 2 m. and a distance of 100 m, showed an average 10 dB loss when the transceivers were placed in or across the corn rows [54].
# A Matheuristic for Joint Optimal Power and Scheduling Assignment in DVB-T2 Networks +
## 3 Strengthening the Initial MILP Formulation
If there exists no assignment of discrete power levels in P to \(s_{1},s_{2}\) that can satisfy the two previous constraints, then coverage variables \(x_{t_{1}s_{1}}\) and \(x_{t_{2}s_{2}}\) cannot be activated at the same time and the following is a valid inequality:  
\[x_{t_{1}s_{1}}+x_{t_{2}s_{2}}\leq 1. \tag{10}\]  
We can identify the inequalities in Equation (10) through pre-processing and include them in the model, obtaining a remarkable strengthening, as discussed in [57]. We denote by DVB-PI+, the power-indexed model DVB-PI strengthened by including the power-indexed GUB cover inequalities in Equation (9) and the conflict inequalities in Equation (10).
## 6 Real Jamming Results Analysis
### Low Jamming Scenario
#### 6.1.2 Second Trajectory  
For this trajectory, the GPS DF-based solution suffers from a partial outage, as shown in Figures 23 and 24. By using the GPS TF signals, the positioning solution performance is slightly improved. Additionally, by using the GPS TF + Galileo SF signals, the positioning solution improves compared to the GPS TF-based solution. By using the Galileo TF signals along with the GPS TF signals, the positioning solution performance is significantly improved compared to the GPS TF counterpart.  
Like the first trajectory, the inclusion of just the GLONASS SF signals does not improve the positioning solution performance. However, adding the GLONASS DF signals to the GPS TF and the Galileo TF signals improves the performance of the positioning solution. This leads to a better positioning solution, especially during jamming intervals, for the GPS TF + Galileo TF + GLONASS DF solution. The main reason is that the number of visible satellites is more than 15 when using GPS, Galileo, and GLONASS signals, compared to 10 when using GPS and Galileo signals, as shown in Figure 25.  
Figure 23: (**a**) Horizontal positioning error for different GNSS signal combinations [second trajectory]. (**b**) Zoomed view of horizontal positioning error for different GNSS signal combinations [second trajectory].  
Figure 24: (**a**) Vertical positioning error for different GNSS signal combinations [second trajectory]. (**b**) Zoomed view of vertical positioning error for different GNSS signal combinations [second trajectory]. [second trajectory].  
Figure 25: Number of visible satellites for different GNSS signal combinations [second trajectory].  
The statistical analysis presented in Table 7 indicates that the GPS DF-based positioning solution offers the worst performance. Additionally, the GPS TF-based solution improves positioning performance, compared to its GPS DF counterpart. The GPS TF + Galileo SF-based solution shows a slight improvement compared to the GPS TF counterpart. Moreover, using the GPS TF + Galileo TF signals improves the RMSE of the positioning solution by about 22% and 50% for the horizontal and vertical positioning components, respectively, compared to the GPS TF counterpart. Adding the GLONASS SF signals to the GPS TF signals and Galileo TF signals does not improve the positioning performance compared to the GPS TF + Galileo TF counterpart. This is because of the effect of jamming insertion on L1 frequency. However, using the GLONASS DF signals with both the GPS TF signals and the Galileo TF signals provides a reliable and accurate positioning solution compared to a GPS TF + Galileo TF-based positioning solution. The RMSE improves by about 22% and 44% for the horizontal and vertical positioning components, compared to the GPS TF + Galileo TF-based solution.
## 4 Impact of Network Latency on Teleoperation
### Network Latency Characteristics  
Network quality in a system (e.g., teleoperation) can be defined by its QoS and quality of experience (QoE) [31; 32], also known as objective and subjective factors, respectively. The objective factors include the network performance parameters. Meanwhile, the subjective factors include user performance parameters such as user interface, video quality, driving precision, comfort level, manoeuvre security, and interruptions [32].  
Network latency is a QoS component that produces a time delay when transferring data across a communication network [33; 34]. Usually, it is calculated as end-to-end delay or round-trip time (RTT), which refers to the time taken for data transfer from source to destination and back again to the source. Networks with longer delays resulting in a noticeable lag have high latency. A sufficient amount significantly affects the application system performance, and more elevated levels can cause system failures.
## 2 Materials and Methods
### Photostability Monitoring  
Working solutions of each squaraine dye at a concentration of 20 uM were prepared by diluting dyes' stock solutions in DMSO at 1 mM. To each well of a 96-well plate was added in quadruplicate 100 uL of each working solution. Squaraines were repetitively irradiated with suitable LED system equipment, which characterization is presented in the next section. Dyes absorbance was measured at the maximum absorption wavelength of each dye in DMSO for 60 min using Multiscan Go spectrophotometer microplate reader (Thermo Fisher Scientific, Vantaa, Finland).
## 3 Results
### ANN-GA Model
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**POP** & **c1** & **c2** & **RMSE** & **R2 Training/R2 Testing** \\ \hline \multirow{8}{*}{1.0} & & 2.0 & 0.04131 & 0.9351/0.416 \\ \cline{3-5}  & & 2.25 & 0.01476 & 0.99223/0.8026 \\ \cline{3-5}  & & 2.5 & 0.0197 & 0.9856/0.7467 \\ \cline{3-5}  & & 2.75 & 0.03924 & 0.9342/0.287 \\ \cline{3-5}  & & 3 & 0.054687 & 0.8867/0.4591 \\ \cline{3-5}  & & 2.0 & 0.045245 & 0.9266/0.4316 \\ \cline{3-5}  & & 2.25 & 0.022922 & 0.9538/0.2011 \\ \cline{3-5}  & & 2.5 & 0.056847 & 0.8958/0.5906 \\ \cline{3-5}  & & 2.75 & 0.077929 & 0.8343/0.8343 \\ \cline{3-5}  & & 3.0 & 0.076957 & 0.8008/0.4151 \\ \cline{3-5}  & & 2.0 & 0.044721 & 0.8967/0.0098 \\ \cline{3-5}  & & 2.25 & 0.071197 & 0.8147/0.5837 \\ \cline{3-5}  & & 2.5 & 0.077373 & 0.8473/0.1413 \\ \cline{3-5}  & & 2.75 & 0.10129 & 0.7439/0.4286 \\ \cline{3-5}  & & 3.0 & 0.077423 & 0.6865/0.5952 \\ \cline{3-5}  & & 2.0 & 0.058303 & 0.8173/0.8076 \\ \cline{3-5}  & & 2.25 & 0.083255 & 0.7477/0.6418 \\ \cline{3-5}  & & 2.5 & 0.087441 & 0.7232/0.3682 \\ \cline{3-5}  & & 2.75 & 0.11482 & 0.6368/0.4699 \\ \cline{3-5}  & & 3.0 & 0.085532 & 0.6164/0.5917 \\ \hline \multirow{8}{*}{2.5} & & 2.0 & 0.091206 & 0.7648/0.6358 \\ \cline{3-5}  & & 2.25 & 0.090042 & 0.6172/0.5633 \\ \cline{3-5}  & & 2.5 & 0.087173 & 0.6741/0.5435 \\ \cline{3-5}  & & 2.75 & 0.088626 & 0.7058/0.3009 \\ \cline{3-5}  & & 3.0 & 0.099032 & 0.6889/0.6418 \\ \cline{3-5}  & & 2.0 & 0.021273 & 0.9873/0.2203 \\ \cline{3-5}  & & 2.25 & 0.0275 & 0.9649/0.1527 \\ \cline{3-5}  & & 2.5 & 0.02423 & 0.9816/0.7212 \\ \cline{3-5}  & & 2.75 & 0.032906 & 0.9656/0.4996 \\ \cline{3-5}  & & 3 & 0.04202 & 0.9388/0.8849 \\ \cline{3-5}  & & 2.0 & 0.054646 & 0.8894/0.656 \\ \cline{3-5}  & & 2.25 & 0.046345 & 0.9284/0.6364 \\ \cline{3-5}  & & 2.5 & 0.054039 & 0.9181/0.6483 \\ \cline{3-5}  & & 2.75 & 0.075662 & 0.8366/0.8086 \\ \cline{3-5}  & & 3.0 & 0.097492 & 0.6866/0.2661 \\ \cline{3-5}  & & 2.0 & 0.062218 & 0.8775/0.7024 \\ \cline{3-5}  & & 2.25 & 0.11006 & 0.698/0.2057 \\ \cline{3-5}  & & 2.5 & 0.088353 & 0.6806/0.413 \\ \cline{3-5}  & & 2.75 & 0.075375 & 0.8243/0.5263 \\ \cline{3-5}  & & 3.0 & 0.1124 & 0.5894/0.6809 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Cont. It emerged that the ANFIS-PSO hybrid model having a population size of 50, the acceleration factor of c\({}_{1}\) = 1.0, and the acceleration factor of c\({}_{2}\) = 2.25 yielded better results (RMSE of 0.01476, training R\({}^{2}\) of 0.9923, and testing R\({}^{2}\) of 0.8026). In Figure 16, the prediction error and the training behavior of the ANFIS-PSO training model are plotted against the sample number.
## 1 Introduction
### Path Planning  
Once the contour is obtained by slicing, it needs to be further planned on its basis to generate a complete path. Maintaining the continuity of the path is important and can influence the forming process of the part [14; 15]. Neighboring layers are usually transitioned with empty travel, and an unavoidable start-stop at the starting point not only leads to defects at the seam interface but also prolongs the build time. [PERSON] et al. [16] proposed a helical upward path generation method to improve the surface quality in such areas. The principle is to use two consecutive layered contours to construct direction vectors from the current layer to the next layer, and these vectors are used to articulate neighboring contours into a helical pattern in space. [PERSON] et al. [17], to improve the problem that helical paths are not capable of filling the area, broke through the limitation of the traditional 2.5-axis application and proposed a new helical filling path generation method with more desirable surface quality for the equipment that has extra rotary axes. [PERSON] et al. [18] continued to utilize the potential of helical paths in conjunction with a multi-axis strategy by improving the method and applying it to a rotary table with two rotary axes, demonstrating the good suitability of helical paths for thin-walled structures by forming unsupported overhanging parts.
## 3 Results
### Parameter Correlations
#### 3.3.1 Thermal-IAQ Correlations  
The correlation between thermal comfort and indoor air quality (IAQ) is positive and moderate, with a correlation coefficient of 0.489. This suggests that, in the analyzed data, an improvement in one of these parameters tends to coincide with improvements in the other. A possible explanation for this relationship could be that both thermal comfort and IAQ are closely linked to occupant satisfaction and well-being. For instance, maintaining a comfortable thermal environment might often require proper ventilation, which could, in turn, enhance indoor air quality by reducing the concentration of indoor pollutants.  
However, the correlation is not perfect, indicating that other factors also play a role in determining both thermal comfort and air quality. For instance, personal preferences and the thermal load from various building elements (e.g., HVAC systems, insulation) could affect comfort levels independently of IAQ. Additionally, while a higher concentration of CO\({}_{2}\) is often associated with poor indoor air quality, it may not necessarily lead to a significant thermal discomfort, as different environmental and architectural factors influence these two parameters separately.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**School Type** & **Number of Schools** & **Number of Classrooms** & **Avg. Classrooms/School** & **\% of Total Schools** \\ \hline Kindergarten & 13 & 448 & 34.46 & 23.64\% \\ University & 37 & 3055 & 82.57 & 67.27\% \\ Vocational & 2 & 0 & N/A & 3.64\% \\ Primary & 1 & 0 & N/A & 1.82\% \\ Secondary & 2 & 6695 & 3347.50 & 3.64\% \\ \hline \hline \end{tabular} N/A = not applicable.  
\end{table}
Table 2: Distribution of schools and classrooms by school type.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Parameter** & **Thermal** & **Lodor Air** & **Acoustic** & **Visual** & **Relative** & **Air Velocity** \\  & **Comfort** & **Quality** & **Comfort** & **Comfort** & **Humidity** & **Air Velocity** \\ \hline Thermal Comfort & 1.000 & 0.489 & 0.172 & \(-0.060\) & 0.434 & 0.205 \\ Indoor Air Quality & 0.489 & 1.000 & 0.090 & \(-0.123\) & 0.649 & 0.418 \\ Acoustic Comfort & 0.172 & 0.090 & 1.000 & 0.259 & 0.278 & 0.076 \\ Visual Comfort & \(-0.060\) & \(-0.123\) & 0.259 & 1.000 & \(-0.139\) & \(-0.294\) \\ Relative Humidity & 0.434 & 0.649 & 0.278 & \(-0.139\) & 1.000 & 0.471 \\ Air Velocity & 0.205 & 0.418 & 0.076 & \(-0.294\) & 0.471 & 1.000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Correlation matrix of IEQ parameters from analyzed data.  
In summary, while thermal comfort and IAQ are correlated to some degree, this relationship should be further explored to understand the specific factors and conditions that govern this interaction across diverse indoor environments.
## 4 Algorithm Design
### Resource Adjustment Within A Category
#### 4.4.1 Fine-Tuning of UAVs' Positions  
The closer the drone is to the IoT device, the smaller the channel loss between the two, resulting in higher channel capacity. Therefore, the purpose of fine-tuning the UAV positions is to bring them closer to the IoT devices with the minimum channel capacity. For the \(n\)-th UAV, the \(t\)-th-category IoT device,\(\forall n\in\{1,\dots,N^{U}\}\), \(\forall t\in\{1,\dots,T\}\), this fine-tuning can be expressed by the following formulas:  
\[x_{n}^{u^{\prime\prime}} =x_{n}^{u^{\prime}}+d_{nt}^{x}f_{bt}^{x} \tag{23}\] \[y_{n}^{u^{\prime\prime}} =y_{n}^{u^{\prime}}+d_{nt}^{y}f_{bt}^{y} \tag{24}\]  
and  
\[d_{nt}^{x} =(x^{I_{train}}-x_{n}^{u^{\prime}})/|x^{I_{train}}-x_{n}^{u^{ \prime}}| \tag{25}\] \[d_{nt}^{y} =(y^{I_{train}}-y_{n}^{u^{\prime}})/|y^{I_{train}}-y_{n}^{u^{ \prime}}|\] (26) \[f_{nt}^{x} =\text{random}(0,c_{3}\times r\times|x^{I_{train}}-x_{n}^{u^{ \prime}}|)\] (27) \[f_{nt}^{y} =\text{random}(0,c_{3}\times r\times|y^{I_{train}}-y_{n}^{u^{ \prime}}|) \tag{28}\]  
where \(x^{I_{train}}\) and \(y^{I_{train}}\) represent the horizontal and vertical coordinates of the device with the minimum channel capacity in \(I_{t}\), and \(x_{n}^{u^{\prime}}\) and \(y_{n}^{u^{\prime}}\) represent the horizontal and vertical coordinates of the UAV after fine-tuning. \(c_{3}\) is a value between \([0,1]\), with a default value of 0.5. \(r\) is also a dynamic parameter between 0 and 1, which is the decay rate parameter. The determination of the value can be understood in the subsequent introduction of the decay rate mechanism, presented in Section 4.5. Through continuous iteration, the position of the UAV can persistently approach the IoT device with the minimum channel capacity.
# RCA-PixelCNN: Residual Causal Attention PixelCNN for Pulsar Candidate Image Lossless Compression
## 3 The Proposed Methods
### Network Architecture  
As depicted in Figure 3, the proposed model primarily comprises four stages: feature extraction, residual learning, causal residual attention learning, and adjustment of output feature channels using 1 \(\times\) 1 causal convolution. The blue block represents the big kernel causal convolution layer, which includes type A convolutional layers for extracting relevant features. The green block represents the small kernel causal layer, which includes type B convolutional layers to expand the receptive field. The orange block represents the residual-causal-attention block, which is our innovation and can be used to capture global feature information; detailed descriptions can be found in Section 3.2. The gray module represents the residual causal block, which can extract fine-grained features. Finally, there is a \(1\times 1\) causal convolution layer that can reshape the resulting features to the desired size.