esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Characterization of Invariant Relative Orbits
### Siegel-Moser Canonical Transformation
From Equation (30), the reader can verify the following properties:  
* the transformed variables \(w_{1}\), \(w_{2}\), \(z_{1}\), and \(z_{2}\) depend only on the in-plane components \(Q_{1}\), \(Q_{2}\), \(P_{1}\), and \(P_{2}\);
* the transformed variables \(w_{3}\) and \(z_{3}\) depend only on the out-of-plane components \(Q_{3}\) and \(P_{3}\);
* the imaginary part of \(w_{1}\) and \(z_{1}\) is null: \(\Im(w_{1},z_{1})=0\);
* \(z_{i}=-j\bar{w}_{i},\quad i=2,3\). In fact, the last property implies that \(|w_{2}|=|z_{2}|\) and \(|w_{3}|=|z_{3}|\), a condition that, as seen in the following section, allows the parametric characterization of relative trajectories in terms of only four parameters.
## 4 Experiments
### Implementation Details  
In our experiments, we report the results of detection models on DOTA-v1.0, DIOR-R, HRSC2016 datasets. WDARFNet is developed based on the MMRate [50] platform and built within the framework of Oriented RCNN [13]. To ensure a fair comparison with other methods, we follow the same data augmentation strategy as other mainstream approaches [7; 13]. In ablation studies, we adopt a scheme of training from scratch to improve experimental efficiency. To achieve higher accuracy on the aforementioned datasets, we pretrain the backbone network on the ImageNet-1K [51] dataset for 120 epochs and then fine-tune it on remote sensing datasets. We use the AdamW [52] optimizer to train the model for 36 epochs on the HRSC2016 dataset and 24 epochs on the DOTA-v1.0 and DIOR-R datasets. The initial learning rate is set to 0.0001, and the weight decay is 0.05. Model training is conducted using 2 RTX3090 GPUs (batch size of 2), and testing is performed using a single RTX3090 GPU.
# Investigation of Red Clay Internal Stress Anisotropy and Influence Factors
## 3 Results and Discussion  
### The Influence of Overlying Load on Stress Paths  
As depicted in Figure 4, the permeability coefficients of red clay in both directions decrease with increasing overlying load, with a faster decrease in vertical permeability. At any given overlying load, the vertical permeability exceeds the horizontal; furthermore, with increasing load, the curves of both directions tend to converge. This demonstrates the anisotropic nature of water permeation in red clay, with both horizontal and vertical permeability coefficients showing a linear negative correlation with overlying load on a semi-log scale, though vertical permeation is more sensitive to changes in load. That is, red clay has higher permeability in the vertical direction, which may be related to the development of vertical pores within the structure of the red clay itself, and the effect of stress effects also has a certain influence on the direction of water stress.  
In order to assess the effect of consolidation stress on the anisotropic stress of undisturbed red clay, the ratio of permeability coefficients (kV/kH) is utilized as a metric. As shown in Figure 5, taking the covering layer load of 150 Kpa as the characteristic boundary point, the variation of kV/kH values forms a two-stage development characteristic. When the overburden load is 25 kPa, the maximum value of kV/kH is 2.78. With the gradual increase of the overburden load, the value of kV/kH initially decreases linearly in a steep manner. After the overburden load exceeds 150 kPa, the rate of decrease gradually slows down and eventually stabilizes at about 1.77. And when the covering layer load is 150 kPa, the value of kV/kH reaches the maximum of 1.92 in the second stage. This indicates that at lower overburden loads, the permeability of red clay primarily depends on the effects of stress effects and the characteristics of voids; when the overburden load is in a lower range, the developed vertical void structure within the red clay enhances the strength of water flow under the influence of stress effects. When the overburden load is large, the pore structure within the red clay is destroyed, the soil structure becomes denser, the porosity greatly reduces, and the effect of stress effects is no longer significant, at this time, the flow rate of water in both horizontal and vertical directions tends to be consistent, thereby showing a more stable permeability coefficient ratio.  
Figure 4: Change of permeability coefficient with overlying load.
## 4 Materials and Methods
### Construction of Plant Expression Vectors  
To investigate the role _OpWRKY3_ played in camptothecin biosynthesis, transgenic plants overexpress and repress vectors of the _OpWRKY3_ gene were constructed. For the construction of the _OpWRKY3_ overexpress vectors, the full length of _OpWRKY3_ was cloned from the cDNA library and inserted into the modified vector pCAMBIA2300\({}^{\text{sm}}\) located at the restriction site of _Spe_ I and _BstE_ II to generate pCAMBIA2300\({}^{\text{sm}}\)-OpWRKY3 (Figure S6) [33]. For the construction of the _OpWRKY3_ repress vectors, the _OpWRKY3_ gene was linked to an SRDX domain for gene expression repression based on the Chimeric REpressor gene-Silencing Technology (CRES-T) to make a chimeric gene (_OpWRKY3-SRDX_) construct, with the SRDX domain linked to the C-terminal end of _OpWRKY3_[48]. The _OpWRKY3-SRDX_ was amplified by PCR with gene-specific primers pCAMBIA 2300\({}^{\text{sm}}\)-OpWRKY3-SRDX-KF-Spel and 2300\({}^{\text{sm}}\)-OpWRKY3-SRDX-KR-BstEII and cloned into the pCAMBIA2300\({}^{\text{sm}}\) vector (Table S1). A corresponding empty vector was considered as the control.
# Detecting Deepfake Voice Using Explainable Deep Learning Techniques
## 4 Methods
### Speech Reconstruction with the Griffin-Lim Algorithm  
The Griffin-Lim algorithm is an algorithm generally used as a phase reconstruction rule-based vocoder before the neural vocoders [22]. It uses the redundancy of short-time Fourier transform with two projections of the spectrogram. The Griffin-Lim algorithm only utilizes the consistency of short-time Fourier transform and excludes any prior information. The algorithm reconstructs the spectrogram with a given amplitude \(A\) to the audio signal by the following process:  
\[X^{[m+1]}=P_{\text{C}}(P_{A}(X^{[m]})) \tag{6}\]  
where \(X^{[m]}\) is a spectrogram at iteration \(m\), \(P_{\text{C}}\) and \(P_{A}\) are metric projections of the set of the consistent spectrogram and spectrogram with a given amplitude. The metric projections are given as:  
\[P_{\text{C}}(X) =\text{GG}^{\dagger}X \tag{7}\] \[P_{A}(X) =A\odot X\odot|X| \tag{8}\]  
where G and G\({}^{\dagger}\) represent, respectively, short-time Fourier transform and inverse short-time Fourier transform. The optimization problem of the Griffin-Lim algorithm is obtained as following:  
\[\min_{X}||X-P_{\text{C}}(X)||_{\text{Fro}}^{2}\text{ s.t. }X\in A \tag{9}\]  
where \(||\cdot||\) is the Frobenius norm. With the optimization by iteration, the spectrogram converges. Although various other reconstruction algorithms or neural vocoders are available, because of its simplicity, the Griffin-Lim algorithm is adopted instead. In this study, we considered the attribution score as a spectrogram and recomposed it back to audio to intuitively confirm the possible characteristics.  
Considering the general behavior of the attribution scores on energy, the attribution score was expected to reflect the general form of the originating audio and its spectrogram. This greediness was also found in image classification, highlighting seemingly random positive-negative attribution scores mostly on the high-contrast contour, regardless of the object-background segmentation. Despite less-than-perfect segmentation, the interference of human visual perception enables XAI methods to provide interpretability. We were inspired to apply the idea of current XAI methods assisted with human perception, which have been used on visual tasks as well as audio tasks.
## 3 Results
### Effect of Wind Fields on Upwelling
The annual average time for the upwelling to respond to the local wind field changed in the central and western South China Sea is 38.9 h, as shown in Figure 8. The response time was longer in the OU_1 sea area, with an average of 39.2 h, while in the OU_2 and OU_3 waters the average response times were 63.4 h and 14.2 h, respectively. The OU_1 sea area had a higher sensitivity to the local wind field changes and the response time was lower than in the OU_2 sea area. The OU_3 sea area had the lowest intensity of upwelling events and its structural stability was weak, making it more susceptible to external environmental factors. Its sensitivity to local wind field changes was high, resulting in a shorter response time. The response time of upwelling currents in the central and western South China Sea to local wind field changes varied with latitude. The response time was longest in the central sea area (OU_2), and then it decreased with the change of latitude. The response time shortened slowly as the sea's latitude decreased, with a decrease of 12.1 h for a latitude decrease of 1\({}^{\circ}\). In contrast, the response time shortened rapidly as the sea's latitude increased by 1\({}^{\circ}\), with a reduction of 24.6 h.  
In conclusion, the intensity and movement of upwelling in the central and western South China Sea were primarily influenced by the local wind field elements, including wind speed, direction, and stress curl. The lag effect of upwelling on the local wind field changes varied with the region, with the OU_1 sea area showing the highest sensitivity to local wind field changes and the OU_3 sea area being the most susceptible to external environmental factors. The response time of upwelling currents to local wind field changes varied with latitude, with the response time shortening rapidly as the latitude increased.
## 1 Introduction
In real time, the captured video is compared with a pre-trained model to monitor the work order and detect poor assembly according to the work instructions [23]. The aim is to provide workers with real-time information on productivity, quality and lead time efficiency, as well as to enable them to conduct self-inspections. This approach helps to create a safe working environment by reducing workers' faults. The information is displayed on a screen for easy access and provides a more efficient and accurate way to monitor the assembly process. The paper is structured as follows. Section 2 provides an explanation of the assembly process, the object-recognition algorithm and the problems that need to be addressed. In Section 3, we describe YOLO, the configuration of hand position/point datasets for regression with the YOLO algorithm and the configuration of regression algorithms and pre-processing. Sections 4 and 5 cover the system configuration used in the experiment, the experimental results and our analysis of the results. Finally, in the conclusion, we summarize our findings and offer recommendations for future research.
# Designing Reenacted Chatbots to Enhance Museum Experience
## 6 Analysis and Results
### Museum Experience: MES  
#### 6.2.1 Chatbot  
Engagement:  
As shown in Figure 3, there were significant differences in all relationships between the three models, with the king scoring the highest, followed by the docent and the QA (\(p<0.001\)). In other words, when a chatbot is used to listen to the explanation related to the exhibition, the degree of engagement clearly varies depending on the chatbot model. This variation has a comprehensive impact on the elements set up based on the design of the chatbots. For the presentation, one participant said in an interview, (P12) \"Since it was an incident that [PERSON] [PERSON] experienced in person, I was able to concentrate better because it seemed to tell a story.\"  
Knowledge/Learning:  
There was a significant difference when comparing the king with the docent (\(p<0.001\)) and the king with the QA (\(p<0.001\)). This means that the docent and QA received similarly high scores, while the king scored relatively low. Many participants stated in post interviews that the docent was more professional and reliable when delivering knowledge. Regarding the QA model, one participant also highlighted the advantages of the QA in conveying knowledge, stating, (P25) \"I liked the QA model better than the other models because it was simpler and easier to read than the long and detailed description of the other two models.\" In general, the docent had a higher knowledge/learning score. This can be supported by the study of embodiment, which has shown that people tend to feel that a source is more reliable because of its elements [31].  
Meaningful Experience:  
This factor is an indicator of the meaningful experience created by appreciating the exhibition. There was no significant difference in this factor, which was expected to be due to limitations in the experimental environment. A meaningful experience is often dependent on interpersonal differences, such as interest in content or background knowledge. However, in this experiment, the model and contents were counterbalanced to reduce the impact on content.  
Emotional Connection:  
This factor had the same score and significance (\(p<0.001\)) as the engagement model. One participant (P5) emphasized the importance of historical reenactment, saying, \"When learning about the motivation or decision-making process for events, I have a richer understanding and empathy of events when they are explained by [PERSON] rather than by the docent.\"  
As a result of analyzing how the museum experience varies depending on the chatbot model, engagement and emotional connection, two of the MES factors, were found to be significant in the order of the king, the docent, and the QA. In terms of knowledge/learning,  
Figure 2: UEQ results on the three chatbot models. (significance \(p\)-value ***: \(p<0.01\), ***: \(p<0.001\)).  
the ranking was reversed to the docent, the QA, and the king. To obtain a closer look at how some of the characteristics of the chatbot model affect users, we divided the users by type of learning style and analyzed the results.
# Artificial Intelligence Enabled Routing in Software Defined Networking
## 1 Introduction
### Software-Defined Networking
OpenFlow switches handle arriving packets by checking if any flow entries in their flow tables match the new arriving packets and whether or not to perform forwarding. A flow table consists of a set of flow entries. Using the OpenFlow protocol, the controller in the control plane can add, update, and delete flow entries in the flow table(s) of an OpenFlow switch via the OpenFlow channel. Figure 2 shows the six main components of flow entry, namely, match fields, priority, counters, instructions, timeouts, and cookies [12]. \"Match fields\" are used to match the ingress port number and part of the information contained in the packet header. Flow entries match packets in \"priority\" order, to the first matching entry being used in each table. If a matching entry is found, then \"instructions\" associated with the specific flow entry are executed. \"Counters\" are updated simultaneously when packets are matched. If no match is found, then the forwarding decision will depend on the configuration of the table-miss flow entry. \"Timeouts\" indicate the maximum amount of time or idle time before the expiration of the flow entry. The \"cookie\" falls under opaque data, which may be used by the controller to filter, modify, or delete flow statistics, but not to process packets.
## 4 Discussion
### Field Trial  
Traps are essential for pest monitoring, but adult captures do not always accurately reflect insect population densities or predict fruit infestation levels [55]. The earliest traps for SWD consisted of plastic bottles with liquid bait, followed by commercial container-type traps using the same bait [20]. Both designs pose challenges for counting and identification, as flies often degrade in the liquid medium.  
For instance, our field evaluation of encapsulated bait confirmed the strong preference of SWD for the WVM bait compared to the control (Figure 5), supporting previous research conducted in Brazil and Italy [40,61]. A total of 78.5% of flies were captured in traps baited with WVM bait, while only 21.5% were caught in control traps. These findings reinforce the effectiveness of this bait for monitoring SWD in cherry orchards. In addition, these results become even more remarkable when the fly population on the plants is extrapolated to one hectare, making the bait's attraction power much more numerically evident. In terms of sex distribution, 95.3% of the captured flies were females, while only 4.7% were males. This disparity can be attributed to the greater attraction of immature reproductive females to fermented baits rather than ripe fruits, whereas males use traps primarily to locate mates [62]. Monitoring data revealed that October had the highest SWD captures. Additionally, during this month, most varieties at the Maquehue Experimental Field were in bloom, likely enhancing olfactory stimuli that attract SWD. Although temperature is a fundamental factor for the increase in the SWD population in a given time, there are other equally important factors that contribute to this effect such as humidity, photoperiod, available diet, and pressure from predators or competitors [63, 64, 65].  
With respect to the effect of cherry varieties on SWD capture, the Lapins variety showed the highest number of captured individuals, followed by Regina and Stella. These findings align with the study by [PERSON] [66], which reported high SWD infestation rates in Lapins and Regina varieties in the Nuble Region, leading to significant economic losses. Preliminarily, the use of microencapsulated bait proved effective in preserving the bait's attractiveness and enhancing SWD capture. This new format, combined with delta-type adhesive traps, offers a valuable tool for generating SWD population curves in southern Chile, which is crucial for informed pest control decision-making [62].
# Turning Video Resource Management into Cloud Computing
## 3 The Video Resource Management System in HDFS-Based Cloud Computing  
### System Layers  
The model of video resource management service systems (Figure 3) consists of five layers from top to bottom: the client, middleware, application server, storage, and infrastructure layers. (1) The client layer is responsible for receiving various requests of users and displaying results on available web browsers, such as Internet Explorer (IE), Firefox, and Chrome. (2) The middleware layer is used to manage computer resources and network communications. It is a connector between the client and application layers, so applications can run across heterogeneous hardware and software platforms.  
Figure 1: The architecture of Hadoop File System (HDFS).  
Figure 2: The Map/Reduce paradigm.  
(3) The application service layer can implement all kinds of services including user management, video searching, and video playing. (4) The storage layer virtualizes the infrastructure resources as a file system and provides distributed data storage services for users. HDFS is employed to implement the function of this layer. (5) The infrastructure layer consists of devices for computing, storing, and communications (such as the hard disk and memory).
# A Secure Link-Layer Connectivity Platform for Multi-Site NFV Services
## 3 Description of the L2S Platform
### Deployment and Configuration Aspects  
Regarding the deployment of the L2S platform, we assume that each site supports external communications following a layer-3 approach (i.e., VNFs can gain access to external networks via an edge router at every site). In addition, a number of virtual links exist at each NFVI (they can be pre-created using the VIM that manages the NFVI resources). These virtual links will be used to support the inter-site communications of the VNFs running on the NFVIs. The deployment of the L2S platform as a multi-site network service results in the instantiation and configuration of an L2S VNF at every NFVI. Upon instantiation, each L2S VNF is connected to the virtual links pre-created at its NFVI, as well as to a virtual link providing connectivity towards an edge router of the site.  
Following the ETSI NFV standards [25], each L2S VNF will automatically be configured through the VNF Manager (VNFM) associated with it. This configuration includes the assignment of access ports to VLANs at the VNF, as well as the creation of IP tunnels towards other L2S VNFs. We want to observe that the specific day-1 configurations to be done on each L2S VNFs will depend on service-level agreements established by infrastructure providers. These agreements will determine the configuration parameters for any L2S VNFs, including VLAN identifiers, network addresses allocated to IP tunnel endpoints, and cryptographic keys to initialize the security mechanisms that will protect IP tunnels. In addition, providers may make unilateral agreements with other providers to configure specific VLANs for the sole purpose of their inter-site communications. These VLANs would be enabled on the layer-2 switch provided by the L2S platform, resulting in the configuration of different VLANs at each L2S VNF.
# Comparative Analysis of Volatile Compounds in Flowers of Different _Actinidia_ Species
## 2 Results and Discussion
### Male Versus Female A. arguta and A. kolomikta Genotypes Comparison  
_Actinidia arguta_'s female genotypes were characterised by a composition of volatiles similar to that of male ones (Figure 4a,b). However, less compounds (39) were detected in female ones. The reason could be due to the smaller number of genotypes, considering that most compounds in male samples were detected in only one genotype. Among female genotypes, important compounds were also represented by ilac compounds (Table 1). Their overall content varied between 29.5% for 'Geneva' and 85.4% for the 'Bingo' cultivar. The latter figure was much higher than the one observed in other varieties. As 'Bingo' is a crossing of _A. arguta_ male and _A. arguta_ with red fruit (previously classified as _A. arguta_ var. _purpurea_), our previous observation of male genotypes was confirmed to the effect that such combination represents a higher content of linalool derivatives. In our analysis, as in that of [PERSON] et al. [23], a higher percentage of linalool was observed in male genotypes. A relatively higher content of phenylethyl alcohol was noted in female than in male genotypes.  
Interestingly, different esters were identified in flowers of male and female genotypes (Figure 4c,d). Only one ester (butanoic acid, 3-methyl-, 3-methylbutyl ester) was detected in both sexes, but still not in every genotype. No aldehydes, ketones or acids were found in female _A. arguta_ genotypes. The composition of alcohols was smaller in female flowers, but high amounts of 1-Butanol and 3-methyl (main alcohol of male flowers) were also found in female genotypes.  
A comparison of male and female clones of _A. kolomikta_ showed almost no differences. Two compounds found in a female clone--that is, 9-Octadecenoic acid (Z), methyl ester, and 12,15-Octadecadienoic acid, methyl ester--were not detected in the male one. On the other hand, one compound, namely furan 3-(4-methyl-3-pentenyl), was found only in male flowers. The differences between sexes in _A. kolomikta_ were observed in the relative content of the main compound, beta-myrcene (e.g., 74.7% in the male and 67.2% in the female clone), and in a few other aromatic terpen, like beta-citronellol, trans-geraniol and nerol (Table 1).  
Summing up, the female flowers of _A. arguta_ may have a simpler composition of volatiles than the male ones, but the observation of _A. kolomikta_ suggests that two randomly chosen genotypes of this species may be very similar to each other.