esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Characteristic VOCs of Silage Based on HS-GC-IMS  
Using GC-IMS technology to obtain three-dimensional, two-dimensional, and color difference spectra revealed differences in the VOCs between normal and abnormal silage under different flavor conditions. Based on the fingerprint spectrum results, the silage samples exhibit distinct characteristic VOCs.  
Compounds such as 3-hexenyl acetate and salicylic acid methyl ester in abnormal silage demonstrate stronger signal intensities in the GC-IMS spectrum. In many plants, methyl salicate (MeSal) is a naturally emitted compound during stress events, making it a key marker for early plant disease detection [29]. [PERSON] et al. identified salicylic acid methyl ester as the primary volatile component in Securidaca long pedunculate (Polygalactace) through GC/MS analysis [30]. MeSal functions as a volatile signaling molecule and is recognized for its aromatic qualities, reminiscent of the scent of wintergreen [31]. This indicates that these compounds are produced by several different pathways compared to the metabolites typically found in normal silage fermentation. Additionally, common VOCs such as ethanol, ethyl ethanoate, ethyl formate, isopentyl isovalerate, ethyl 2-methyl-2-pentenoate, and 2-methyl-2-pentenoate were also identified. Some VOCs appear as monomers and dimers, including ethyl heptanoate, ethyl hexanoate, propanol, ethyl pentanoate, butyl iso pentanoate, and 3-hydroxy hexanoate, due to varying concentrations and characteristics. Regarding the metabolic pathways leading to the biosynthesis of volatile compounds, aldehydes can convert into their corresponding alcohols, such as 1-butanol, 1-hexanol, and 1-propanol. Acids (hexanoic acid, 3-hexenyl acid) will produce corresponding ethers (3-hexenyl acetate, ethyl acetate) during fermentation, and they are also known to play a significant role in dehydration during the wine-making process to produce wines with elevated aromatic profiles [32]. The presence of substantial amounts of organic acids in silage can rapidly lower the pH of the environment, promoting the production of various esters in conjunction with aldehydes [33]. For example, ethanoic acid, which is the second most abundant acid in silage, can react with 1-hexanol to generate ethyl hexanoate and ethyl acetate when combined with ethanol. The literature indicates that esters exhibit high odor intensity ratios (OIRs) and are among the most active compounds in the fermentation process [34]. Commonly reported esters include butyric acid ethyl ester, hexanoic acid ethyl ester, and octanoic acid ethyl ester [35], which are recognized as potent odorants that impart sweet, floral, and fruity fragrances.
## 3 Building Information Modeling Applied to Seismic Vulnerability
### Mechanical Characterization of Materials and Rapid Vulnerability Analysis  
The survey on the masonry quality has shown masonry with variable quality, depending above all on the time of construction (Figure 4). In this case, the calculation of a safety factor SF (%) (Equation (2)) is not required, but we can assume that: for class A masonry, SF can be considered greater than 80% of the value required by law; for class B masonry, it can be considered in the range of 50%-75%; while for class C masonry, it can be considered between 25% and 50% of the value required by law. The walls tested, except for a couple of cases, show a medium/high behavior to the transmission of vertical loads, and some criticalities for the transmission of out of plane loads on the most deteriorated panels.  
The rapid vulnerability assessment showed a good overall behavior for the whole building (\(SF_{VG}\)\(>\) 80%) and also a good behavior of the wall leaves which, in case of an earthquake, could be subject to tilting mechanisms (\(SF_{VL}\)\(>\) 80%). On the other hand, some roof and inter-storey floors showed problems in transmitting vertical loads (60% \(<\)\(SF_{VS}\)\(<\) 40%).  
The presence of non-structural elements that could cause vulnerability, such as flue pipes or balconies and walkways, together with poor floor-masnorry anchoring, led to a vulnerability level of type B2 (Table 3).  
The surveys, crack pattern observation and the rapid evaluations which were carried out have indicated some important shortcomings, worthy of diagnostic investigation. Therefore, the following were carried out:  
* Geo-radar surveys in some rooms of the Cascina to understand the direction of floor structural elements when covered with a hatched ceiling, to verify the wall thickness as well as the foundations depth and size. The foundations investigation has also confirmed the probable difference in the construction period of some parts of the building [27; 28];
* Sonic investigations on some walls, in those areas where the crack pattern showed fissures of doubtful interpretation, in order to investigate the panel homogeneity and identify possible detachments of internal leaves and/or surface plasters;
* Superficial and deep penetrometric tests for mortars characterization;
* Bricks characterization based on the behavior detected on bricks preserved on site and probably contemporary to the ones in use. The results obtained were compared with values available in the literature for materials of the same type and period [29; 30; 31];
* Penetrometric tests (\(\copyright\)Resistograph) to characterize the timber floors and roofs [32; 33; 34].  
Normally, a focused diagnostic phase such as the one described above affects less than 6% the cost of the building's safety intervention.
## 3 Materials and Methods
### Ibc  
Currently, two IBC techniques are known: capacitive and galvanic, which were analyzed in [31]. Capacitive coupling does not depend on external wires, but the quality of transmission is influenced by the environment. For capacitive coupling, a ground connection between the input and output circuits is necessary. The signal is transmitted between the body channel of the transceivers via a current loop, which consists of a transmitter electrode, body channel, receiver electrode, and a capacitive return path through the external ground.  
Galvanic coupling operates without a connection to the external ground. High-frequency electromagnetic waves are generated at the input. They propagate throughout the body and reach the receiver electrodes. This method does not require external wires, and the transmission quality does not depend on the environment. Two transmitting electrodes generate signals, and the body is used as a waveguide for signal propagation. Then, the signal is received by two receiver electrodes.  
The main difference between these two techniques is that the IBC channel characteristics in capacitive coupling are highly dependent on the environment around the body, whereas in galvanic coupling, they depend on the physical characteristics of the human body.  
IBC data transmission offers the following advantages:  
* Security: IBC provides a secure and private communication network, offering natural security and interference-resistant communication [32]. IBC requires a significantly lower operating frequency compared to RF systems. This means that signals are limited to human proximity, so data reading requires body contact [33].
* Energy efficiency: research has shown that IBC consumes an order of magnitude less energy, at data rates up to 10 Mbps, making it an attractive communication technique for WBAN applications [24,34].
* Frequency reuse: IBC forms a near-field communication network inside and around the human body. Consequently, it allows for the reuse of the same frequency band by other WBAN users.
###### Abstract  
Many deep learning (DL)-based detectors have been developed for optical remote sensing object detection in recent years. However, most of the recent detectors are developed toward the pursuit of a higher accuracy, but little toward a balance between accuracy, deployability and inference time, which hinders the practical application for these detectors, especially in embedded devices. In order to achieve a higher detection accuracy and reduce the computational consumption and inference time simultaneously, a novel convolutional network named YOLO-DSD was developed based on YOLOv4. Firstly, a new feature extraction module, a dense residual (DenseRes) block, was proposed in a backbone network by utilizing a series-connected residual structure with the same topology for improving feature extraction while reducing the computational consumption and inference time. Secondly, convolution layer-batch normalization layer-leaky ReLu (CBL) \(\times\)5 modules in the neck, named S-CBL\(\times\)5, were improved with a short-cut connection in order to mitigate feature loss. Finally, a low-cost novel attention mechanism called a dual channel attention (DCA) block was introduced to each S-CBL\(\times\)5 for a better representation of features. The experimental results in the DIOR dataset indicate that YOLO-DSD outperforms YOLOv4 by increasing mAP\({}^{0.5}\) from 71.3% to 73.0%, with a 23.9% and 29.7% reduction in Params and Flops, respectively, but a 50.2% improvement in FPS. In the RSDD dataset, the mAP\({}^{0.5}\) of YOLO-DSD is increased from 90.0-94.0% to 92.6-95.5% under different input sizes. Compared with the SOTA detectors, YOLO-DSD achieves a better balance between the accuracy, deployability and inference time.  
optical remote sensing; object detection; feature extraction; attention mechanism +
Footnote †: journal: Applied
## 4 Experimental Results and Discussion
### Potential Anomaly Detection
As shown in Figure 18a,b, the ratio of entry of the two stations is 2.72% and 9.52%, the ratio of exit of the two stations is 14.29% and 9.52%. In the Beijing West Railway Station area, the total ratio of public transportation entry and exit is 12.24% and 23.81%. It can be judged that although these two cards have visited Huoying or Lishuiqiao, they have frequent activities near Beijing West Railway Station.When a card is an abnormal card, it will also be reflected in the origin-destination chord view, as shown in Figure 18c. Figure 15: Travel feature values of card numbers 13080401 and 54287878 (**a**) Card number 13080401, (**b**) Card number 54287818. Figure 16: Passenger travel time pixel matrix view (**a**) Card number 13080401, (**b**) Card number 54278818. Figure 17: Passenger 2D travel trajectory view (**a**) Card number 13080401, (**b**) Card number 54278818. Figure 18: Passenger origin-destination chord view (**a**) Card number 13080401, (**b**) Card number 13080401, (**c**) Card number 54278718. From the travel vehicle bubble view, we can also see some clues. In Figure 19b, there is a bubble in the bubble view of the travel number 52427718. It can be found that the passenger took subway line 10 about 20 times on 4 August. This behavior is very suspicious and consistent with the characteristics of abnormal behavior. This is the place where 54278818 is more unusual than 130800401. In addition, the vehicle selection with card number 130800401 is confusing, with no rules, which is consistent with the travel characteristics of abnormal behavior, as shown in Figure 19a. In summary, it can be concluded that they are most likely to be the abnormal passengers active near Beijing West Railway Station and have been to Huoying and Lishuiqiao. This case can verify that our method is very effective in potential abnormality detection.
## 2 Materials and Methods
### Analysis by ICP-MS
In our study, antioxidant activity was evaluated using ABTS spectrophotometric tests that evaluate the activity of both hydrophilic and lipophilic antioxidants, while the FRAP test uses antioxidants as  
Figure 1: Content in phenols, flavonoids and antioxidant activity (FRAP and ABTS) in multifocal honeys collected from three different agro-climatic zones (plains, hills and mountains). reducing agents in a redox-linked colorimetric method in order to reduce the Fe\({}^{3+}\)/Fe\({}^{2+}\) couple [40]. In general, variability in the antioxidant capacity values was found among the honeys studied (Figure 1). In particular, the highest FRAP antioxidant activity was observed in mountain and hill honeys at 2.75 and 2.67 \(\pm\) 0.1 \(\upmu\)mol TE/g, respectively, while the lowest activity was observed in the plain honey samples at 1.61 \(\pm\) 0.06 \(\upmu\)mol TE/g (\(p\) < 0.001). The same trend was observed for the ABTS values, which were significantly lower in the honeys produced in the plains (2.12 \(\pm\) 0.08 \(\upmu\)mol TE/g; \(p\) < 0.001), while the mountain honeys had the highest value (6.12 \(\pm\) 0.22 \(\upmu\)mol TE/g) although not statistically different from those of the hills (5.94 \(\pm\) 0.19 \(\upmu\)mol TE/g; \(p\) > 0.05). These results are in line with what was observed by [PERSON] et al. [41] in Nepalese honeys. The observed variations in the contents of phenolic components were predictable since the amounts of phytochemicals, such as phenolic acids and flavonoids, in honey are strictly related to the botanical origin, as highlighted by numerous investigations. Furthermore, plant metabolism changes according to the agro-climatic zone, influencing the composition of the nectar that bees feed on for honey production [42]. Furthermore, [PERSON] et al. [43] highlighted that the antioxidant capacity of honey is also influenced by non-phenolic compounds, such as vitamins and amino acids, but also by antioxidant enzymes such as glucose oxidase and catalase.
## 3 Results
### Dynamic Analysis
#### 3.3.2 Velocities  
Based on the results obtained from the dynamic analysis, the velocities over a considered time period were also determined. The relationship between the corrugation amplitude and the maximum velocity of the element over a given period of time for the considered thicknesses of a steel sheet is shown in Figure 16.  
The analysis of dynamic velocity over time for plates of various thicknesses and corrugations reveals several insights similar to those observed in the deflection analysis. Firstly, as shown with the static analysis, thinner sheets demonstrate a greater relative gain in performance for a given corrugation.  
Secondly, the graphs also show the superposition of two waves--one associated with external forcing and the other with the natural frequency of the plates.  
A key observation in reference to the velocity analysis is that, beyond a certain corrugation amplitude, the differences in velocity responses become less pronounced. This suggests that there is a threshold of corrugation, beyond which, further increases in amplitude do not significantly impact the dynamic velocity of the plates.  
Figure 15: The relationship between the corrugation amplitude and the maximum deflection of the element over a given period of time for the chosen thickness of a steel sheet: (**a**) \(t\) = 3.0 mm; (**b**) \(t\) = 4.0 mm; (**c**) \(t\) = 5.0 mm; and (**d**) \(t\) = 6.0 mm.
## 1 Introduction
Region growing methods and the clustering algorithm are the most used in literature for segmentation purpose. Region growing approaches (e.g., [15]) usually start with a selected seed point, calculate its properties, and compare them with adjacent points based on certain connectivity measures to form the region. Alternatively, the cluster techniques (e.g., [7; 11]) first summarize the variability in the data by computing the attributes for all points and then group data that cluster together. Each point in the point cloud is classified into one of the clusters of predefined number based on its distances to the clusters' centroid. Other approaches, such as the Hough Transform [16] and random sample consensus (RANSAC) [17], can be used to extract straight lines from boundary points. Finally, the voxel-based algorithm (e.g., [18]) divides a point cloud into voxels with equal size, then the neighboring voxels with elevation differences of less than a threshold are classified iteratively into the same subset and segmented from other points. This study aims to present an investigation and critical comparison of two different fully automatic approaches for roof segmentation used in 3D building reconstruction. In particular, we present a stable solution approach (a), described in Section 2.1, for building roof extraction based on a fuzzy c-means [19] that uses a potential-based clustering method for initial clusters center determination and clusters number determination [20; 21]. At the end of the clustering processes, a density-basedand connectivity analysis, as proposed by [7], is used to improve the results of the above clustering process through separation of the planar and coplanar planes. Moreover, a second approach (b), based on a region growing segmentation method [22] using smoothness constrain and curvature consistency refined with application of RANSAC [23] to remove any potential over-segmentation issues, is described in Section 2.2. The roofs extracted by these two segmentation approaches were used for 3D building reconstruction. After the extraction of the boundary points, as described in [24], a 2.5D dual-contouring approach, proposed by [25], was adopted to create vertical walls connecting the extracted rooftops to the ground. Both the proposed approaches have been evaluated (Section 3) in terms of geometry accuracy against the real measurements in two different case studies (in terms of types of urban development and ALS data input data resolution) in Matera (Italy) and Toronto (Canada). The results indicate that both approaches have precisely reconstructed the geometric features of the test building preserving topology. In particular, the approach (b) based on region growing segmentation has exhibited slightly better performance but required a computational time that is double that of the clustering-based approach (a). Finally, Section 4 presents the conclusion and main remarks on the investigation presented in this paper.
## 3 User Study
### Devices
#### 3.2.2 Augmented Reality (AR) Interaction Interface  
In the Augmented Reality (AR) domain, the interaction interface takes a different form, relying primarily on natural hand gestures and movements. AR headsets are equipped with sophisticated sensors and cameras that track the user's hands and gestures in real-time. This enables users to interact with virtual objects and overlay digital information onto the physical world seamlessly. AR interfaces often leverage intuitive gestures, such as swiping, pinching, and tapping, to manipulate virtual content. Users can interact with these virtual elements directly within their natural field of view, fostering a sense of presence and integration with the real world. AR interfaces excel in offering an intuitive and visually unobtrusive means of interaction. They capitalize on the user's innate understanding of physical gestures, making them accessible to a wide range of users without the need for specialized controllers. However, challenges may arise in achieving precise interactions in complex AR environments, where recognizing fine-grained gestures accurately can be demanding.
## 3 Results and Discussion
### Bioactivity  
Bioactivity of the scaffolds was assessed by soaking in SBF for 1 and 3 days, respectively (Figure 7). The results demonstrated that there was a lot of spherical precipitate with bonelike apatite shape on the scaffolds containing nHAp. Besides, most of the scaffold's surface was covered by the apatite precipitate after soaking for 3 days when the nHAp increased to 15 wt %. The scaffolds containing 20 wt % nHAp was covered completely by the apatite precipitate at that time. The precipitate did not appear on the control group. As multiple studies have pointed out, there is no apatite formation on the pure CaSO\({}_{4}\) during soaking in SBF [32; 33], which coincides with the experimental result in this test. The EDS analysis (Figure 8a) further demonstrated that the precipitate layer on the scaffolds was mainly composed of calcium (Ca) and phosphorus (P). Their ratio was about 1.74, which is close to the stoichiometric ratio of Ca/P in the apatite (1.67).  
Figure 6: Compressive strength and fracture toughness of the scaffolds at different nHAp contents. Significant difference between the composite scaffolds and pure CaSO\({}_{4}\) scaffolds (\({}^{*}\)_p_ < 0.05, \({}^{**}\)_p_ < 0.01).  
Figure 7: Morphologies of the scaffolds containing (**a,f**) 0 wt % (control); (**b,g**) 5 wt %; (**c,h**) 10 wt %; (**d,i**) 15 wt %; and (**e,j**) 20 wt % nHAp after soaking in SBF for (**a–e**) 1 day and (**f–j**) 3 days.  
In addition, composition of the scaffolds with 15 wt % nHAp before and after soaking was also analyzed by FTIR spectroscopy (Figure 8b). The intensity of PO\({}_{4}\)3- absorption bands, located at around 567 cm\({}^{-1}\) and 1035 cm\({}^{-1}\), presented a significant increase. Moreover, the CO\({}_{3}\)2- absorption bands were observed at 1418 cm\({}^{-1}\) in the scaffolds after soaking, which did not appear in the scaffolds before soaking. All of these results confirmed that the bonelike apatite formed on the scaffold after soaking.  
The nHAp played an important role in the apatite formation, which was able to serve as the nucleation site for the apatite crystal growth in SBF. The apatite layer in the biological environment could greatly promote the formation of strong bone bonds between the scaffolds and bone tissues, as well as bone regeneration progress [34; 35]. Besides, the apatite, covering the scaffolds, slowed the degradation rate of the scaffolds by reducing its contact with the soaking solution.
# An Energy-Efficient Routing Algorithm
## 5 Cs-Culm
### Clustering Algorithm  
The location information in the threshold setting of NEW-LEACH [11] results that sensor nodes away from the sink node have a lower probability of becoming cluster heads. The lacking information will be unfavorable for data reconstruction in CS. Therefore, we remove the location information to make the distribution of cluster heads as homogeneous as possible. The cluster heads can be chosen stochastically based on the algorithms, as follows.  
\[T(n)=\left\{\begin{array}{cc}\frac{qW_{1}(E_{c}/E_{0})}{1-q\left(r\,\text{ mod}\left(\frac{i}{i}\right)\right)}+\frac{qW_{2}e^{-\frac{(E_{b}-E_{c})}{E_{0}}}}{1-q \left(r\,\text{mod}\left(\frac{1}{i}\right)\right)}&\text{if }\forall n\in G\\ 0,&\text{if }\forall n\
otin G\end{array}\right. \tag{10}\]  
where \(E_{c}\), \(E_{0}\), \(E_{b}\) are the rest energy, the initial energy, and the energy consumed by the former round, respectively. \(q\) is the probability of becoming a cluster head. \(W_{1}\) and \(W_{2}\) are the weight of rest energy and energy consumption rate, and the relationship between them is  
\[\left\{\begin{array}{cc}W_{1}+W_{2}=1\\ W_{i}\geq 0\,i=1,2\end{array}\right. \tag{11}\]  
When the selection of cluster heads is finished, cluster heads will broadcast cluster information with broadcast radius \(r\). Then common nodes inter-cluster will forward their packets to cluster heads randomly for data fusion. This is completed by a Bernoulli random generator. If a node participates in inter-cluster transmission with probability \(p\), the quantity of nodes \(n_{1}\) will obey a binomial distribution \(n_{1}\sim B(N_{1},p)\), in which \(N_{1}\) is the number of nodes in one cluster head.