esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Experimental Results
#### 3.1.3 Influence of Distances \(l_{b}\) and \(l_{c}\)
The results from the \(h=200\) mm series show that the ultimate loads measured for beams with small dowel spacings (\(l_{c}=4d\)) do not differ significantly from the loads reached for beams with a single dowel placed at mid-span, and the larger the dowel spacing, the higher the splitting load. Small increases in the \(l_{c}\) spacing lead to load increases, but this tends to stabilise at large \(l_{c}\) spacings. A similar trend is observed for the series with \(h=100\) mm, although many of the specimens in this series failed in bending (Table 4). It should be noted that the average values of the loads reached are practically identical between the two groups, except for the series with an \(l_{c}\) spacing of \(710\) mm, where the specimens with \(h=100\) mm showed lower splitting capacity. In this case, it seems that \(\alpha\)  
Figure 12: (a) Splitting failure loads of single-dowel connections versus the distance between the connection and the support \(l_{b}\), for the two different beam depths; (b) splitting failure loads of two-dowel connections versus dowel spacing \(l_{c}\), for the two different beam depths. does not influence the ultimate load, even though the series with \(h=100\) mm have twice the \(\alpha\) ratio of those with \(h=200\) mm. It is interesting to note that series with \(l_{b}/h<2.4\) failed by splitting and those with \(l_{b}/h\geq 2.4\) were susceptible to fail by bending. Furthermore, when \(l_{b}/h\geq 4.40\), the governing failure mode appears to be primarily bending. However, these values are influenced by the number of dowels with which the connection is loaded, as beams with two dowels can transfer more load to the beam before splitting occurs. This favours bending as the dominant failure mode.
## 3 Results and Discussion  
### Comparisons of Different Fusion Strategies and Structures  
We used VGG-M-2048 directly as the global network. Our work focused on designing the local network. Because fusion methods and numbers of convolutional layer can both influence the recognition rate. We compared different numbers of the convolutional layer and fusion methods to determine the network structure.  
Figure 4 gives the comparison results. The recognition rate given in Figure 4 is the final recognition rate of the whole network. It is seen that in all cases of varying number of layers, the concatenation method can give the best final recognition rate. This may indicate that the concatenation method can better fuse the global and local features and is more suitable in this research. It is also observed that when the number of convolutional layers reaches two, the recognition rate has the maximum value. From the point of two layers, the recognition rate will decrease slightly if the number of layers increases. This may be because a two layer convolutional network is enough for the medium-sized database in this research, and therefore, more layers become redundant. Thus, we chose two convolutional layers structure in the local network and concatenation fusion methods in this research. This design is based on the experiment results and also accords with the inference (in Section 2.2.2) that the local network have shallower structure than that of the global network.
## 1 Introduction
In fact, we have addressed some dependability issues for scalar and visual wireless sensor networks in previous works. In Reference [10], a formal methodology was proposed to model some aspects of hardware and networking failures, focused on scalar sensors. Then, in References [10; 11], visual coverage failures were mathematically modelled and processed for dependability assessment, although no dependability evaluation was performed due to the lack of a proper method for this task. In this article, however, we leveraged the mathematical formulations in those work to achieve a more complete model based on different coverage failures that are considered at once. We provide adaptations in the methodology proposed in Reference [10] in order to incorporate this more complete model and consequently to evaluate WVSN in a more comprehensive way. The resulted model and the performed simulations are important contributions that enhance the knowledge in this field, significantly supporting new developments in industrial WSN. Therefore, the expected contributions of this article can be summarized as follows:  
* Integrate different methodologies proposed in previous works, defining a comprehensive unified mathematical model to assess the dependability of wireless visual sensor networks, specially considering the particularities of sensors-based industrial applications;
* Create a consistent reference for quality assessment in critical applications, associating the most relevant failure causes in visual monitoring, which has not been considered before by the literature;
* Discuss how the parameters of the proposed mathematical model can be achieved in practical applications, potentially supporting more effective dependability evaluations;
* Demonstrate a practical use of the proposed methodology considering realistic parameters, which can be easily replicated when evaluating other applications in industrial scenarios. The remainder of the article is organized as follows. Section 2 presents related works regarding dependability evaluation and coverage failures in wireless visual sensor networks. Fundamental concepts for this work are presented in Section 3. The proposed methodology is described in Section 4, detailing the different evaluation phases. Section 5 presents and discusses important results considering a study case in an industrial scenario. Finally, the conclusions are stated in Section 6.
# Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera
## 3 Results
### Comparison with Previous Findings  
Results from this work were comparable with a highly similar work done by [PERSON] et al., (2017) that used three different segmentation algorithms, namely fully convolutional network (FCN-8s), fully convolutional DenseNet (Tiramisu), and Pix2 Pix networks, as well as [PERSON] et al., (2021) who applied Deeplab and UperNet networks for semantic segmentation. The trained models were then applied to several images with different backgrounds instead of just one specific location. Mean IoU was used to evaluate the segmentation results since both authors included IoU evaluation in their works. For the study carried out by [PERSON] et al., (2021), only the highest IoU results were included. The comparison between findings from this work and the previous study is reported in Table 2. The comparison shows that the proposed approach (DeepLabV3+) obtained higher IoU results over the work of [PERSON] et al. (2029) and [PERSON] et al. (2020).  
Figure 11: Visualization and comparison of segmentation results during inference phase. (**a**) Original image. (**b**) DeepLabv3+ prediction. (**c**) SegNet prediction.
## 2 Materials and Methods
### Finite Element Simulation
#### 2.2.1 Geometric Model  
Comsol Multiphysics version 5.5 software (COMSOL Co., Ltd., Shanghai, China) was used in the simulation, and the microwave antenna model, water-cooled circulation system and lung tissue model were constructed, respectively.  
The ablation probe (antenna) is made of slender coaxial cable. In order to manufacture the antenna, a small annular groove with specified width is cut on the outer conductor near the short-circuit end of the antenna to allow electromagnetic wave to propagate into the tissue. This design uses a 1 mm wide crack [24]. In addition, the antenna was inserted into the PTFE catheter to prevent the antenna from adhering to the dry ablation tissue. The geometric model and size of the microwave antenna are shown in Figure 3 and Table 2[25], and the microwave frequency is 2450 MHz.  
Figure 3: Microwave antenna model.  
In this study, an ideal lung model was constructed. Since the ablation area is axisymmetric, the geometric model is simplified to a two-dimensional model. In this model, the lung tissue is idealized as a regular rectangle, and the pig lung is assumed to be isotropic and homogeneous tissue. The microwave antenna is inserted 100 mm into the pig lung tissue, as shown in Figure 4.
## 2 Materials and Methods  
The procedure for manufacturing the MPCM was in situ polymerization [5]. The two kinds of MPCM had different polymeric shells: poly(methyl methacrylate) (PMMA) and polystyrene (PS), respectively. For both types of shell, the core of the PCM was tetracosane, C\({}_{24}\)H\({}_{50}\), with a melting temperature of around 50 \({}^{\circ}\)C. Moreover, two encapsulation mass ratios (shell:core), 1:3 and 1:1, were tested. The average diameter for all microcapsules was 1-2 \(\upmu\)m.  
MMA monomer and ethyleneglycol dimethacrylate crosslinker were both obtained from Merck Company (Darmstadt, Germany). They were used after NaOH solution (10 wt % in water) treatment to remove monomethylether hydroquinone inhibitor. Terbutylhydroperoxide initiator from Merck Company (Darmstadt, Germany) was used as received. Ferrous sulphate heptahydrate (FeSO\({}_{4}\).7H\({}_{2}\)O) and ammonium persulphate (Sigma-Aldrich company, St. Louis, MO, USA) were also initiators and used as received. Triton X-100 was used as surfactant, and was supplied by Sigma-Aldrich (Darmstadt, Germany).  
In this study, mechanical tests were performed using the Atomic Force Microscopy (AFM) technique with a Peak Force Quantitative NanoMechanics mode (QNM) instrument (Terrassa, Spain).  
A Multimode 8, Nanoscope \(V\) electronics system was used. Additionally, a diamond AFM probe MDNISP with a nominal spring constant of 388 nN\(\cdot\)nm\({}^{-1}\) was also used. The sensitivity was 68.2 nm\(\cdot\)V\({}^{-1}\), and the set point was between 400 and 550 nN. The peak force amplitude was 150 nm.  
Sample preparation consisted of the previous elaboration of slurries by mixing manufactured MPCM with ethanol. Then, 50 \(\upmu\)L of slurry was poured on freshly cleaved mica and gently dried with a nitrogen stream. The vertical force required to plastically deform the microcapsules was tested in triplicate for both PMMA:tetracosane and PS:tetracosane samples. Furthermore, topographic images before and after each indentation were taken. These images before and after performing the tests were acquired for each sample at 23 \({}^{\circ}\)C and 60 \({}^{\circ}\)C to compare the properties of both behaviors when the PCM was in solid state (below its phase change) and liquid state (above to its phase change).
# Non-Local Spatial and Temporal Attention Network for Video-Based Person Re-Identification
## 2 Related Works
### A Video-Based Person Re-ID Pipeline  
In our article, we follow the state-of-the-art structure that has been summarized by previous researchers and is the most commonly used base structure for video-based person Re-ID works. It mainly consists of two parts: (1) Feature extraction: This part can extract meaningful abstract spatial representations from video frames through pre-trained ImageNet Models [26; 27], such as residual network (ResNet50 [26]) and squeeze-and-excitation residual network (SE-ResNet50 [27]). (2) Temporal feature aggregation: In this part, the frame-level features extracted in the previous step are aggregated into video-level features. [PERSON] et al. [28] summarized that the feature aggregation method could be roughly divided into three types: Average temporal pooling (TPavg) operation, temporal attention (TA), and the RNN layer. [PERSON] et al. [29] compared different feature extraction methods and temporal feature aggregation methods. The comparison results are shown in Table 1. Two main conclusions can be drawn from the comparison results: First, the choice of the backbone network will affect the overall performance of the system, and SE-ResNet50 has a better performance than ResNet50. Second, TPavg is superior to attention/RNN. Therefore, we chose SE-Resnet50 + TPavg as the baseline of our work.
## 2 Materials and Methods
### DNA Extraction, Sequencing, and Bioinformatics Analysis  
For sample preparation, rice grains were collected via three-layer and five-point sampling methods. Then, 5 g of each sample with three replicates per treatment was transferred to EP tubes. The microorganisms on the rice grain surface were extracted with sterilized water under sterile operating conditions. The V3-V4 region of the bacterial 16S rRNA gene was amplified using primer sets 338F (5\({}^{\prime}\)-ACTCCTACGGGAGGCAGC A-3\({}^{\prime}\))/806R (5\({}^{\prime}\)-GGACTACHVGG GTWTCTAAT-3\({}^{\prime}\)). In addition, the ITS1 region (internal transcribed spacer) of the fungal communities was amplified using primer sets ITS1F (5\({}^{\prime}\)-CTTGGTCATTTA-GAGGAAGTAA-3\({}^{\prime}\))/ITS2R (5\({}^{\prime}\)-GCTGCGTTCT TCATCGATGC-3\({}^{\prime}\)) [19]. The amplified products were detected on an Illumina NovaSeq platform (Novogene Co., Ltd., Beijing, China).  
QIIME2 was used to qualify the obtained raw sequences [20]. The chimera sequences were removed from the sequences, which were compared with the Sliva database using the UCHIME algorithm [21; 22]. The clean data were denoised into amplicon sequencing variants (ASVs) via QIIME2. The Sliva database was used to annotate for the representative sequences. R 3.6.0 was used to perform diversity and statistical analyses [23].
## Solution Optimization
### Base Station Antenna Height Optimization  
Link quality between transmitter and receiver plays a major role in the performance of any radio network [9]. The radio performance may be different as crop growing as agricultural application is a dynamic environment. The crop canopy, earth reflection and other factors have an effect on radio wave propagation. We have conducted wireless communication experiments in the sowing stage (3 months), growing stage (3 months) and ripening stage (1 month) in order to ensure reliable transmission performance. Antenna height seems to be an important element for wireless sensor network connection. Figure 2 shows radio range measurements over antenna height in three different crop growth stages. We can see that:  
* Radio range becomes wider with antenna height getting longer in any crop growth stage.
* Radio range decreases as crop grows for any antenna height.  
As the results above show, a suitable antenna height is extremely significant for cropland monitoring in all black pepper growth stages from sowing to ripening. In order to optimize the receiver strength, one needs to decrease the effect of the out of phase signals to the minimal by removing obstacles from the RF Line of Sight. The strongest signal is on the direct line between sender and receiver and always lies in the 1 st Fresnel Zone [10]. The Fresnel zone radius (FZR) \(r\) can be expressed as:  
\[r=17.32\sqrt{\frac{d}{4f}} \tag{1}\]  
where \(d\) is the total distance between antennas in km, \(f\) is the frequency in GHz. It can be tolerated generally that the maximum obstruction is less than 40% of the FZR. Then, antenna heights in cropland applications must satisfy  
\[h\geq h_{c}+60\%\bullet r \tag{2}\]  
where \(h_{c}\) is the average of crop heights. Therefore,  
\[h\geq h_{c}+5.196\bullet\sqrt{\frac{d}{f}} \tag{3}\]  
Figure 2: Radio range over antenna height in three different crop stages.  
The lowest antenna height for farmland applications can be decided according to Equation (3). For instance, a 2.4 GHz RF link with a path length of 100 m in the black pepper sowing stage (\(h_{c}\approx\) 0 m), results in antenna heights of 1.1 m. However, when the black pepper grows in the booting stage (\(h_{c}\approx\) 1.8 m) and jointing stage (\(h_{c}\approx\) 2.4 m), antenna height needs to be 2.9 m and 3.5 m respectively. Consequently, the antenna of each sensor node needs to be placed above at least 3.5 m if we plan to monitor the black pepper from seeding to harvest.
# Determining Thresholds for Optimal Adaptive Discrete Cosine Transformation
## 2 Materials and Methods
### Proposed Adaptive DCT Algorithm
Given that TDV is normalized between 0 and 1, we can evaluate the result of compression with different pairs of threshold values from this diapason, increasing the precision of the search step by step. At the first level of optimization, pairs of threshold values are evaluated with 0.1 accuracy (0.05:0.05, 0.05:0.15,..., 0.95:0.95). For each pair, a quadtree is constructed for the tested image and ADCT is performed to obtain the compression ratio and MS-SSIM between the result and the original image since the latter must be above the minimal threshold for the chosen compression quality. Then, a pair with the best resulting compression ratio is chosen for the next level of search with higher accuracy (0.01, then 0.001). New values are searched for in the vicinity of the previous optimal values. As a result, optimal values are obtained with an accuracy of 3 decimal digits. A visual representation of the search algorithm is shown in \[\underset{pm,ps}{\text{min}}\{f(g(x,y),\widehat{g}_{pm,ps}(x,y))\}. \tag{3}\]0 The grid represents the pairs of threshold values. The colors, from blue to red, represent the resulting compression ratio value after ADCT: blue represents the minimum value on the search level and red represents the maximum value. Black represents invalid threshold value pairs or that resulting MS-SSIM is below the target value for the chosen quality. A threshold  
\[\underset{pm,ps}{\text{min}}\{f(g(x,y),\widehat{g}_{pm,ps}(x,y))\}. \tag{3}\]1 Image quality comparison: (**a**) the original images; (**b**) high-quality ADCT; (**c**) medium-quality ADCT; (**d**) low-quality ADCT. value pair with the biggest compression ratio on one level is searched through at higher precision at the next level, with 3 levels of precision in total. If there are threshold value pairs with similar compression ratios, one with the best MS-SSIM is chosen. However, an equal compression ratio usually means equal quadtree structure and equal MS-SSIM; the first pair by order is chosen then. The image illustrates the search process on all three levels: first the 0.25:0.35 pair is chosen out of the 0.05:0.05 (top left) to 0.95:0.95 (bottom right) field, then 0.27:0.31 is chosen from the 0.21:0.31-0.30:0.40 field, then the leftmost red cell representing 0.267:0.306 is chosen from the 0.266:0.306-0.275:0.315 field.
# Homogeneous Data Normalization and Deep Learning: A Case Study in Human Activity Classification
## 4 Discussion and Conclusions
Furthermore, we would like to thank the Politecnico de Viseu for their support. This work is funded by FCT/MEC through national funds and co-funded by FEDER--PT2020 partnership agreement under the project **UIDB/50008/2020** (_Este traballo e franaciado pela FCT/MEC atraves de fundos nacionais e cofinanciado pelo EDER, no albutio do Acordo de Parcia PT2020 no albutio do projeto UIDB/50008/2020_). This work is also funded by National Funds through the FCT-Foundation for Science and Technology, I.P., within the scope of the project **UIDB/00742/2020**. Furthermore, we would like to thank the Politecnico de Viseu for their support. This article is based upon work from COST Action IC1303-AAPELE-Architectures, Algorithms and Protocols for Enhanced Living Environments and COST Action CA16226-SHELD-ON-Indoor living space improvement: Smart Habitat for the Elderly, supported by COST (European Cooperation in Science and Technology). More information at www.cost.eu. The authors declare no conflict of interest. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Sensor(s)** & **Normalizer** & **Number of Activities Detected** & **Accuracy** \\ \hline
[2] & Accelerometer & MaxAbsScaler & 3 & 53.12\% \\ \hline
[2] & Gyroscope & MaxAbsScaler & 3 & 59.71\% \\ \hline
[2] & Accelerometer & MinMaxScaler & 2 & 46.25\%, \\ \hline
[2] & Gyroscope & MinMaxScaler & 2 & 52.75\% \\ \hline
[2] & Accelerometer & RobustScaler & 2 & 49.79\% \\ \hline
[2] & Gyroscope & RobustScaler & 1 & 50.87\% \\ \hline
[2] & Accelerometer & Z-Score & 2 & 52.71\% \\ \hline
[2] & Gyroscope & Z-Score & 3 & 56.81\% \\ \hline
[2] & Accelerometer & None & 3 & 79.11\% \\ \hline
[2] & Gyroscope & None & 3 & 81.46\% \\ \hline
[50] & Accelerometer & None & 1 & 22.90\% \\ \hline
[50] & \multicolumn{2}{c}{Mean and} & & & \\ Accelerometer & Standard Deviation & 5 & 85.89\% \\  & & (similar to Z-Score) & & \\ \hline
[49] & \multicolumn{2}{c}{Accelerometer and} & & & \\ Magnetometer & None & 2 & 40.69\% \\ \hline
[49] & \multicolumn{2}{c}{Mean and} & & & \\ Magnetometer & Standard Deviation & 5 & 86.49\% \\  & & (similar to Z-Score) & & \\ \hline
[49] & \multicolumn{2}{c}{Accelerometer,} & & & \\ Magnetometer, and & \multicolumn{2}{c}{None} & & & \\ Gyroscope & & & & \\ \hline
[49] & \multicolumn{2}{c}{Mean and} & & & \\ Magnetometer, and & \multicolumn{2}{c}{Standard Deviation} & & 5 & 89.52\% \\  & & (similar to Z-Score) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of results obtained with a previous study.
## 3 Results
Oenochemical Parameters of Vranec and Cabernet Sauvignon Wines Produced by Autochthonous and Commercial Yeast Strains  
The results from the oenochemical parameters of Vranec and Cabernet Sauvignon wines resorting to the autochthonous yeast strains F-8 and F-78 and the commercial yeast strain D-80 for fermentation are shown in Table 4.  
The results from Table 4 indicated a significant difference for the Cabernet Sauvignon wine CS3 (see Table 1 for wine designations) produced with commercial yeast D-80 (13.97%). The percentage of alcohol in the other Cabernet Sauvignon wines (CS1 and CS2) and all Vranec wines (V1-V3) were above 14%. The concentration of total reducing sugars in all six wines were in the range from 2.0 to 2.3 g/L. Nevertheless, the lowest amounts of tittatable acids were obtained in wines from the Cabernet Sauvignon grape variety fermented by the autochthonous yeast strain (F-8) and commercial yeasts strain (D-80), chiefly 5.21 and 5.92 g/L, respectively. The lowest concentrations of volatile acids in wines from both grape varieties were observed in those fermented by autochthonous yeast strains (F-8 for Vranec and F-78 for Cabernet Sauvignon wines). Specifically, the amount of volatile acids in Vranec wine V2 fermented by autochthonous yeast strain F-78 was 0.43 g/L, whereas in wine CS1 fermented by autochthonous yeast strain F-8, it was 0.42 g/L. Overall, the pH values for Vranec wines produced by autochthonous and commercial yeast strains were lower in comparison to the Cabernet Sauvignon wines produced by the same yeast strains. Significant differences among parameters were confirmed by statistical analysis. The effect of yeast strain was significant for all parameters presented in Table 4, while the effect of grape variety was significant only with respect to the variables volatile compounds and tittatable acids. Interaction effect between yeast strain and grape variety was not statistically significant for any of the parameters presented in Table 4.  
In contrast to the oenochemical parameters summarized in Table 4, the influence of yeast strains on the content of total phenolic compounds, total anthocyanins, and color intensity was substantially more noticeable. The results presented in Table 5, unfold the highest amount of total phenolic compounds for wines of both grape varieties when the