esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Digital Twins and Supply Chain  
Digital twins include a physical and a virtual entity, which should have a similar state in the end of the realized twinning. The physical entity is measured or observed, and then realized as a virtual entity. The flow of information is similar: back from the virtual entity back to the real world [17]. The concept of the digital model might be more appropriate for our research, i.e., a system where data flow is not automatic [13]. However, the supply chain itself is not a single distinct entity, but a network of actors. Calling a digital representation of such an entity a digital twin is meaningful, as the concept itself is not well-defined.  
The data flows in mirroring or twinning are presented in Figure 3, as suggested by [PERSON] et al. [17]. Such real-time approaches are not quite feasible for cyber security situations, not least because of the often sensitive nature of the domain data. We discuss the actual solution in the result section of this paper.  
The use of digital twins is somewhat implicit in cyber exercises. The concept for a supply chain digital twin has been discussed elsewhere, e.g., by [PERSON] et al. [18]. They conclude that such twins differ from twins of other--mainly physical--concepts, and that simulation, optimization, and data analytics are the main technologies needed for implementation, although there is no clarity about this in the literature they reviewed. [PERSON] et al. propose a smart supply chain driven by digital twins and highlight a real-use case involving a retailer [19]. [PERSON] describes the design and development of digital twins for supply chains. The presented case study focuses on a pharmaceutical company, and includes simulations related to the supply chain [20]. In their review, [PERSON] et al. [21] emphasize the use of data analytics as the next step for digital twins in the supply chain and logistics.An review articleby [PERSON] and [PERSON] [22] concludes that the concept of digital twins has possibilities beyond the few present uses. They also present a definition of the digital twin for cyber-physical systems, where real-time or historical data is used to build a replica with sufficient fidelity. Studies such as [PERSON] et al. [23] have implemented digital twins with physical testbeds to mirror farmland canal systems. [PERSON] and [PERSON] have created a simulation testbed that was used for supply chain modeling. The solution was based on network flows and featured a user interface [24]. The mentioned  
Figure 3: Theoretical dependencies between a real-world system and a digital twin.  
Figure 2: Elements of a cyber exercise.  
studies illustrate the usefulness of digital twins in testbeds for various domains. However, little literature exists about actual implementations of such systems in the context of supply chains and cyber security.
## 4 Model Evaluation
### Region of Interest Intersection over Union  
The ROI IoU hyperparameter defines the minimum percentage of overlap between the predicted instance and the ground truth to an inference to be classified as positive. This hyperparameter is applied in the ROI Heads region of Mask R-CNN (Section 3.2.5) and its adjustment can lead to a more optimized training process, depending on the similarity that the classified instances have among themselves.  
Table 6 presents the results of the three evaluated models, with RoI IoU values equal to 0.55, 0.57, and 0.60, chosen according to the default value of 0.50 [9]. The results show that the RoI 0.55 and RoI 0.57 models present very similar metrics with a slight superiority of the model with RoI 0.57. The RoI 0.60 model presents a very evident degradation of all metrics, which may lead to a strong bias in the results of some dataset samples.  
Figure 13 shows the loss function curves for models with ROI Heads IoU equal to 0.55, 0.57, and 0.60. Figure 13 shows that high values for the IoU ROI cause the model overfitting with an increase in the validation loss function, approximately after 20 k iterations, and consequent loss of generalization of the algorithm.  
These results points toward a RoI IoU Head parameter of 0.57 as the one with the best performance, showing that a medium overlapping between the ground truth and the predictions are adequate to optimize the performance of the algorithm.
# RF Energy Harvesting Wireless Communications:
## 7 Potential Applications
### 5G-Assisted RF-EHWC  
As investigated in Section 2.1, the power density of ambient RF energy is low due to the high spreading loss of EM waves in the free space and the safety concerns. The 5G mobile technique can alleviate this low power density problem and provide an opportunity for RF-EHWC networks to harvest energy in a more efficient and flexible manner.  
By increasing the communication frequency to the mmWave band, the size of the antenna can be greatly reduced, as illustrated in Figure 21. This enables dense deployment of small femtocells with a large number of antennas in a 5G network [99]. The high-density deployment of the antenna array is beneficial to the application of an RF-EHWC system from the following three perspectives:  
* _Large antenna gain_: Using an array antenna, HBS (femtocells) can create a narrow beam to increase the antenna gain for efficient power transmission or enhance the spatial multiplexing gain for high-speed data transmission. As tested in [100], if both sender and receiver are equipped with the array antenna that is demonstrated on Figure 21, the strength of RF waves arrived at the receiver can be \(20\,\mathrm{d}\mathrm{B}\) higher than the situation with a single antenna.
* _High beam-steering resolution_: The mmWave phased array can precisely form a large number of controllable beams. With a 32-element phased array, the difference between the directions of neighboring beams can be as low as \(1.4\) degrees [101]. The high resolution of the beam-steering offers the HBS a high degree of freedom to allocate power for information and energy transmissions. It is even possible to supply power and send data to adjacent RF-EHWC devices simultaneously. At the receiver side, the RF-EHWC device with massive antennas can harvest energy and receive information from different HBSs at the same time via the antenna-switching RF-EHWC scheme [102; 103].
* _Low propagation loss_: Ultra-dense networking is considered a promising technique for 5G communications [104]. The high-density deployment of the HBS can shorten the distance between the energy source and RF-EHWC devices. Therefore, a low propagation loss of the RF energy can be expected in a 5G-assisted RF-EHWC network.  
Figure 21: Antenna comparisons [105]: Patch antenna for traditional wireless communications in \(3\,\mathrm{GHz}\); Antenna array with \(64\) (\(8\times 8\)) elements for 5G communications in \(30\,\mathrm{GHz}\).
## 4 Results
### Model Validation  
#### 4.3.1 GPP  
In Figure 3, there are 116 sites covering eight biomes used to compare simulated global GPP with observed values (The detailed information of 116 sites in Table S4). Figure 3a represents the compare GPP which simulated by TRIPLEX-CNP, while Figure 3b is the results of GPP simulated by model without P cycle. TRIPLEX-CNP performs better than its predecessor that without P cycle (R\({}^{2}\) = 0.85 vs. 0.77, both \(p\) < 0.01; Figure 3). At all sites, there are 14 sites for deciduous broadleaf forest; 12 sites for evergreen broadleaf forest; 37 sites for evergreen needleaf forest; 4 sites for mixed forest; 24 sites for grassland; 12 sites for shrubland; and 12 for savanna. There is only one site for deciduous needleaf forest since all of the sites were selected by random stratified sampling from 165 sites. For individual biomes, the simulations of savanna, grassland and shrub land are shown to be closer to the observed GPP after adding phosphorus cycle. For the other biomes, modeled GPP was consistently lower than the observed GPP.
# Transfer Accuracy of Two 3D Printed Trays for Indirect Bracket Bonding--An In Vitro Pilot Study
## 3 Results
### Bracket Bonding Accuracy (Linear Measurements)
#### 3.2.2 Impact of Tray Type  
Linear discrepancies were higher for hard (median: 0.13 mm, Q1-Q3: \(-\)0.05-0.27 mm) compared to the soft (median: \(-\)0.08 mm, Q1-Q3: \(-\)0.08-0.26 mm) trays (\(p\) = 0.004). When splitting the data, significant differences were only detected in occlusal/gingival direction within the crowding group, i.e., at canines (\(p\) = 0.005), where the soft tray performed more accurately, and at molars (\(p\) = 0.002), where the soft trays transferred the tubes more gingivally (Table 1).
# Pre-Training Multi-Concept Question Embeddings for Knowledge Tracing
## 2 Related Work
### Question Embedding Representation
#### 2.2.2 Pre-Trained Question Embedding  
Recent studies have isolated question embedding as a standalone task, independent from the knowledge tracing task [21; 22]. This unified approach to constructing question embeddings, which is not task-specific, can be utilized across multiple knowledge tracing models and other downstream tasks like question recommendation and learning path planning. This allows downstream tasks to focus on their core objectives.  
PEBG [23] represents the question-concept relationship as a bipartite graph, using a simple dot product layer to learn low-dimensional embeddings for questions, and demonstrates its effectiveness in question representation through visualization techniques. PERM [24] employs a two-layer attention mechanism to further uncover semantic information in student-question and student-concept interactions from the global student-question-concept relationships.SEEP [25] constructs a heterogeneous graph of student-question-concept from the dataset, decomposing it into two semantic perspectives and using node-level attention mechanisms to obtain embeddings for questions and concepts.BiCo [26] chooses two similar yet distinct perspectives (subjective and objective) as semantic sources for question embedding, designing a pre-training model for question embeddings that achieves state-of-the-art performance on two real-world datasets, proving its robust expressive capabilities.  
Overall, PEBG pioneered the use of pre-training for constructing question embeddings, establishing a fundamental structure for obtaining embeddings from question-concept bipartite graphs and difficulty constraints, thereby laying the groundwork for subsequent studies. However, none of these four models account for the possibility that a single question may involve multiple concepts, nor do they utilize the semantic information readily available from the textual embeddings of concepts. Moreover, by explicitly modeling the multi-concept matrix, we can better serve downstream applications.
## 6 Results and Discussion
### Analysis of Energy Tax  
The AMCTD protocol was tested for the energy tax under four different scenarios, i.e., without and with 4, 8 and 12 attacker nodes. The energy tax value was revealed to be less for the without attacker node scenario as compared to all others having attacker nodes, in Figure 10. It is evident in Figure 10 that active attack significantly increases energy consumption.  
Figure 11 displays the energy tax for the AMCTD protocol under various conditions. According to the findings in Figure 11, the AMCTD protocol has a much lower energy tax for 0, or without attack. Figure 11 makes it clear that an active attack raises the energy tax on the AMCTD protocol by up to 25%.  
Figure 11: Energy Tax of AMCTD without Attack, AMCTD with 4, 8 and 12 Attacker Nodes.  
Figure 10: AMCTD: Energy Tax without attack and with 4, 8 and 12 Attacker Nodes.
# Image Splicing Location Based on Illumination Maps and Cluster Region Proposal Network
## 3 The Proposed Framework
### Training Loss  
The candidate tampered regions acquire fixed-size feature map through Region of Interest (ROI) alignment. The processing of the network is divided into three branches, which are the classification of the fixed size feature map, the regression of the bounding box and the segmentation of the mask. The loss of classifying the real and fake \(L_{cls}\) is expressed as (6):  
\[L_{cls}=-\log(p_{u}) \tag{6}\]  
where \(p_{u}\) is confidence coefficient of the fake class. The loss for regression the bounding box \(L_{bbox}\) is computed as (7):  
\[L_{bbox}=\sum_{i\in\{x,y,w,h\}}\text{smooth}_{L1}(t_{i}-v_{i}) \tag{7}\]  
where \(x\) and \(y\) are the center position of ROI, \(w\) and \(h\), respectively, indicate the width and height. The \(t_{i}\) and \(v_{i}\) are the label and predict of regression box \(i\). Function smooth\({}_{L1}\) () is the improvement of L1 distance, which is computed as (8):  
\[\text{smooth}_{L1}(x)=\left\{\begin{array}{c}0.5x^{2}\text{ if}|x|<1\\ |x|-0.5\text{ otherwise,}\end{array}\right. \tag{8}\]  
The loss \(L_{mask}\) for the predicted mask \(y_{pred}\) and lable mask \(y_{lable}\) is computed as (9):  
\[L_{mask}=-y_{label}*\log\Bigl{(}y_{pred}\Bigr{)}-(1-y_{label})*\log\Bigl{(}1- y_{pred}\Bigr{)} \tag{9}\]  
In summary, the total loss for dual-streams is defined as Equation (10):  
\[L=L_{cls}+L_{bbox}+L_{mask} \tag{10}\]
## 3 Conclusions and Further Perspectives  
HREM was developed in 2006, and for about a decade, operated on self-assembled apparatuses. In 2015, a fully operable, all-inclusive HREM machine became commercially available under the trade name of 3D Optical HREM imaging (OHREM). This increased the number of projects using the HREM technique, which was illustrated by a quadruplication of publications based on HREM data over the last ten years, and it boosted the development of new protocols and technical advancements. However, HREM is still to be considered as a technique in its beginnings with great potential for refinement and improvement. As pilot data has shown, it has a yet-unexplored high potential for visualising a broad variety of organic materials in unmatched resolution and data quality.
# A Comprehensive Survey on Real-Time Applications of WSN
## 2 Types and Requirements of WSN
### Multimedia Wireless Sensor Network  
A multimedia WSN consists of tiny sensor nodes that can sense, compute, actuate, and communicate. A variety of applications of the WSN include habitation monitoring, traffic management systems, and ecological monitoring. The multimedia WSN is a network of wirelessly interconnected devices that are able to ubiquitously retrieve video and audio transmission, still images, and scalar sensor data from the environment [23]. [PERSON] et al. (2006) analyzed the recent developments on multimedia WSNs and outlined the primary research challenges. Algorithms, protocols, and hardware for the enhancement of multimedia WSN sensors were surveyed and open analyses were conducted for the problems mentioned. The authors tend to classify ready-made hardware as an accessible research prototype for the multimedia WSN [24]. [PERSON] et al. (2017) focused on some advanced well known multi channel assignment protocol in WSN. They have listed challenging issues and the classification of different channel selection policies, types of channel assignment, and methods of channel assignment [25].  
Some Key Requirements of Wireless Sensor Networks  
* **Scalability**: It will expand this network to add nodes as required. Its expansions should be easy to conduct.
* **Reliability**: Many methods are there for reducing the power usage of (WSN) nodes, which are resulting in an increase in the lifetime of the network and their consistency.
* **Responsiveness**: The response time is very quick because of its limitations.
* **Mobility**: Mobility is the basic feature of WSN. It is a wireless network so no wire is used for this network. That is why mobility is a key requirement of WSN.
* **Power efficiency**: It uses a sensor that results in low power consumption.
## 4 Creep Strain Fields And Discussions  
The initial 3D image of each tested sample was first taken by using X-ray micrography, and considered as the reference configuration. The parallel and perpendicular samples were then successively subjected to four steps of creep under constant axial stresses while the inclined sample  
Figure 4: (a) 3D image by synchrotron X-ray micro-tomography (with a spatial resolution of \(0.7\times 0.7\times 0.7\) μm3/pixel); (b) 3D distribution of a simplified mineralogy [7; 45].  
Figure 5: Schematic illustration of sample preparation from initial big core and definition of loading angle between bedding planes and sample axis (\(\alpha=0^{\circ},45^{\circ},90^{\circ}\)).  
to three creep steps. Each creep step may have lasted for several weeks. During each creep step, 3D images were taken at different instances. The images obtained at the deformed configurations were compared with the reference ones to compute the displacement and strain fields of tested samples. Some representative results obtained are presented and discussed in this section, in relation with material heterogeneity. However, with the resolution of ISIS-4D platform and the size of samples adopted here, only big mineral particles could be identified in X-ray images.  
All the tests were conducted in a air-conditioned room with a constant temperature of \(15\pm 0.5\)\({}^{\circ}\)C. Further, the stress levels prescribed for different creep steps were calculated as ratios to the corresponding uniaxial compression strength for three loading orientations. For this purpose, three preliminary uniaxial compression tests were first performed to determine the peak strengths. It can be already noticed that the uniaxial compression strength of the inclined sample was significantly lower than those of the parallel and perpendicular samples for which the peak strength is almost identical. Therefore, the failure strength of the COx claystone exhibited a clear anisotropy. As mentioned in many previous studies [20; 23; 25], the small strength of the sample inclined at 45\({}^{\circ}\) was due to the fact that the sliding along bedding planes enhanced the failure of sample. Further, it is worth noticing that the values of uniaxial strength obtained here were higher than the average value reported in previous works [16; 17; 50]. The difference was probably due to the low moisture content of the tested samples. The values of axial elastic modulus and Poisson's ratio are also given in Table 1. One can observe that the elastic properties were also anisotropic. The axial elastic modulus decreased continuously from the parallel to perpendicular directions. The kind of evolution is consistent with many previous studies on sedimentary rocks [20; 23; 25]. The evolution of elastic modulus was generally related to compaction of bedding planes.
## 3 System Model
### SINR Analysis  
To retrieve the original message from the received signal (\(r_{s}\)), the particular user's received signal must be directly decoded, considering other signal components as interference.  
Therefore, to decode \(r_{s}\) signal for User 1 from Equation (5), the instantaneous SINR for User 1 is given as  
\[SINR_{U_{1}}=\frac{P_{T}\alpha_{1}|h_{1}|^{2}}{P_{T}\alpha_{2}|h_{1}|^{2}+P_{ T}\alpha_{3}|h_{1}|^{2}+\ldots+P_{T}\alpha_{N}|h_{1}|^{2}+\sigma^{2}} \tag{7}\]  
where \(|h_{1}|^{2}\) is the channel gain for User 1.  
Similarly, to decode the \(r_{s}\) signal for User 2 from Equation (6), the \(r_{s}\) must decode the User 1 signal and perform SIC as User 1's received signal component is an undesired but dominant signal. Hence, after performing SIC, the resulting \(r_{s}\) will be  
\[r_{s}^{\prime U_{2}}=\sqrt{P_{T}}h_{2}\bigg{[}\underbrace{\sqrt{\alpha_{2}}m_ {2}}_{Desired\ Signal}+\underbrace{\ldots+\sqrt{\alpha_{N}}m_{N}}_{Interfence \ Signal}\bigg{]}+n_{2} \tag{8}\]  
Once User 1's dominant signal component is removed from Equation (6), resulting in Equation (8), the \(r_{s}\) signal can be directly decoded so that User 2 can have the desired and dominant signal after SIC. Hence, other remaining user signal components can be treated as interference. Hence, the instantaneous SINR for User 2 is given as  
\[SINR_{U_{2}}=\frac{P_{T}\alpha_{2}|h_{2}|^{2}}{P_{T}\alpha_{3}|h_{2}|^{2}+ \ldots+P_{T}\alpha_{N}|h_{2}|^{2}+\sigma^{2}} \tag{9}\]where \(|h_{2}|^{2}\) is the channel gain for User 2.  
Therefore, the SINR for the \(i^{th}\) user can be expressed as  
\[SINR_{U_{i}}=\frac{P_{T}\alpha_{i}|h_{i}|^{2}}{P_{T}\alpha_{i+1}|h_{i}|^{2}+ \ldots+P_{T}\alpha_{N}|h_{i}|^{2}+\sigma^{2}}\]  
\[SINR_{U_{i}}=\frac{P_{T}\alpha_{i}|h_{i}|^{2}}{\sum_{i=i+1}^{N}P_{T}\alpha_{j}|h _{i}|^{2}+\sigma^{2}} \tag{10}\]  
where \(|h_{i}|^{2}\) is the channel gain for the \(i^{th}\) user.
## 3 Methodology
### Dataset  
The Anti-UAV Database is a compilation of thermal images specifically collected for the purpose of detecting drones in various environments. This database was assembled as part of the open challenge proposed by the Beijing Institute of Electronic Equipment during CVPR 2023 [31]. The objective of the challenge was to create a standardized dataset that would facilitate the evaluation and comparison of various drone detection models developed by the participants in the competition.  
The database comprises multiple scenarios that simulate real-world situations, consisting of 600 videos corresponding to more than 230,000 thermal images divided into three sets: 70% for training, 15% for validation, and 15% for testing. The images encompass diverse scenarios and lighting conditions, thereby providing a robust foundation for training and evaluating AI models. These images include variations in the size, speed, and shape of the drone. The scenarios incorporate both urban and rural settings, featuring daytime and nighttime conditions, which add complexity to the detection challenge.  
The images were captured under various environmental conditions, including differing temperatures, humidity levels, and visibility conditions. This indicates that the model must be capable of detecting drones in scenarios ranging from clear skies to low-visibility conditions. The database comprises eight types of videos, each representing a specific challenge:  
* UAV: Images that are not categorized by any particular condition.
* Out of View (OV/VE): The target moves out of the current field of view.
* Occlusion (OC): The target is partially hidden behind another object.
* Fast Motion (FM): The target moves rapidly.
* Scale Variation (SV): The scale of the bounding box varies significantly.  
* Thermal and Infrared Crossover (TC/IC): The target temperature is similar to that of another object or the surrounding landscape.
* Dynamic Background Clusters (DBCs): Dynamic background surrounding the target, such as animals or vegetation.
* Low Resolution (LR): The area of the bounding box is very small.
* Target Scale (TS): The target will have a very small or large scale as the frames progress.  
Figure 1 illustrates the distribution of data by video type. It can be observed that the maximum concentration of videos is found in the UAV class, as well as in Thermal Crossover (TC) and Fast Motion (FM). Conversely, three groups exhibit minimal representation; occlusion (OC) comprises a total of 275 images, while low resolution (LR) and Out of Vision (OV) contain no images at all. The latter two are excluded; however, in the case of Out of Vision (OV), it does have representation like (VE), and therefore, these cases are considered.