esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Micro-CT Scans  
To conduct the first micro-CT scan before access cavity and root canal preparation, the samples were fixed on the turntable of the micro-CT device and scanned with the X-ray source of the device at 80 kVp, 125 mA, 26.7 \(\upmu\)m pixel size, and 0.2 rotation. The images obtained after the scan were transferred to the NRecon software (ver. 1.6.10.4, Bruker X-ray, Kontich, Belgium). A total of 1800 two-dimensional sections were obtained from each sample. For the reconstruction, the ring artifact correction was fixed to 7, and the smoothing parameter was set to 3. The beam hardening artifact correction was set to 38%. Contrast settings were kept between 0 and 0.05 for all the samples. Examples of images obtained from micro-CT scanning before cavity and root canal preparation are given in Figure 1.  
Figure 1 shows the results of the first micro-CT scan. In this scan, teeth were examined regarding fractures, cracks, caries, and radii of curvature to avoid missing possible errors in the dental operating microscope. Samples from micro-CT scanning were placed in silicone molds (Express XT, 3M ESPE, Neuss, Germany) with root tips visible to stabilize and isolate them from the environment during access cavity preparation. The experimental groups were formed according to the type of access cavity and the Ni-Ti file system to be used, as shown in Figure 2.  
Figure 2 shows the four groups and the study design. Two different micro-CT scans were carried out for the control and analysis of all groups. The samples used in the study were randomly assigned to four main groups (\(n\) = 15). Of the randomly selected teeth, the groups were as follows: Group 1 included traditional access cavity and root canal preparation using One Reci(TAC-OR) (\(n\) = 15); Group 2 included traditional access cavity and root canal preparation using ProTaper Ultimate (TAC-PU) (\(n\) = 15); group 3 included conservative access cavity and root canal preparation using One Reci (CAC-OR) (\(n\) = 15); and Group 4 included conservative access cavity and root canal preparation using ProTaper Ultimate (CAC-PU) (\(n\) = 15). All specimens were removed from the silicone molds after cavity and root canal preparations and placed in the holder pla  
The images obtained after micro-CT scanning of traditional and conservative access cavities are shown in Figure 3. Figure 3 shows the differences between the access cavities of the two different methods.
# Data Augmentation for Voiceprint Recognition Using Generative Adversarial Networks
## 2 Literature Review
### Generative Adversarial Networks
#### 2.3.3 Objective Function  
GANs use an adversarial training process, where the generator and discriminator are both trying to optimize opposing objectives. The discriminator seeks to maximize the probability of correctly classifying real versus fake data, while the generator tries to minimize the discriminator's ability to tell the difference. This competition is modeled using a minimax objective function, as follows:  
\[\underset{G}{\text{minimax}}V(D,G)=\text{E}_{x\sim p_{data}(x)}[\log(D(x))]+ \text{E}_{z\sim p_{z}(z)}[\log(1-D(G(z)))]\]  
This equation represents the minimax game between the generator and the discriminator, as set out below:  
* The generator (\(G\)) aims to minimize this function, by improving its ability to produce data that the discriminator cannot distinguish from real data.
* The discriminator (\(D\)) aims to maximize this function, by improving its ability to correctly identify real and generated data.  
The objective function is based on the cross-entropy loss, commonly used in classification tasks. As the GAN trains, the generator's ability to produce realistic data improves, while the discriminator becomes more adept at distinguishing real data from fake data. The ideal state, or Nash equilibrium, is reached when the generator can produce data so realistic that the discriminator cannot tell the difference with better than random guessing (i.e., 50% accuracy).  
GANs consist of two neural networks--a generator and a discriminator--that are trained simultaneously in an adversarial setup. The generator creates synthetic samples from random noise, while the discriminator evaluates whether the samples are real or generated. This iterative process improves the generator's ability to produce realistic data while enhancing the discriminator's ability to distinguish between real and synthetic samples.  
The generator aims to minimize the discriminator's ability to differentiate between real and generated samples, while the discriminator strives to maximize its classification accuracy. This dynamic adversarial training is modeled as a minimax optimization problem, where the generator and discriminator iteratively improve by competing against each other. Effective training requires carefully tuned hyperparameters, such as learning rates and batch sizes, to ensure convergence and avoid instability or pattern collapse.  
In this study, the GAN is utilized to augment the voiceprint dataset by generating synthetic audio samples. The training process incorporates mechanisms such as regularization and adaptive learning rates to stabilize the adversarial training and maintain the diversity of generated samples. By combining the synthetic samples with real data, the GAN enhances the training dataset, addressing the challenges of data scarcity and improving the performance of the voiceprint recognition system.
# CRISPR-Cas Systems: Prospects for Use in Medicine
## 5 Alternative Cas Nucleases for Genome Editing  
Cas9 precision and efficiency depend upon many factors, such as: the guide and target sequence [124], DSB repair mechanism (NHE) or HDR template DNA insertion, see Section 3.2) and delivery system (see Section 3.3). Moreover, cytotoxicity is also case-specific for Cas9-based systems. For example, overexpression of Cas9\({}_{\text{Spy}}\) and its catalytically inactive modification dCas9 in _Corynebacterium glutamicum_ lead to cells death even without gRNA expression, which creates limitations for its use as a tool for bacterial genome editing [125].  
A number of CRISPR-Cas9 modifications have been lately developed. As mentioned above, dCas9 can be fused to FokI nuclease domain to increase its specificity [126], or even fused to ZFPs or TALENs [127]. Besides engineering Cas9 to improve its efficiency and specificity, another major area of research focuses on the search for alternative Cas nucleases.  
One of these is the RuvC-like nuclease Cpf1 found in _Prevotella_ and _Francisella_. Cpf1 is a type V CRISPR-Cas nuclease, being the distinctive feature of this class. The main difference of Cpf1 from Cas9 is its ability to cut target DNA forming sticky ends. This increases the efficiency of DNA repair with a homologous template [128]. Cpf1, also known as Cas12a, is currently the most promising tool for mycobacteria genome editing [129].  
Since the specificity of RNA-guided CRISPR nucleases is determined by both the protospacer sequence and the PAM motif, this imposes certain limitations, important for genomic editing using CRISPR-Cas [32; 43]. The effectiveness mostly depends on the positioning accuracy of the double-stranded DNA break. In the case of CRISPR-Cas9, PAM consists of three nucleotides 5'-NGG-3' (or 5'-NAG-3') [32]. Therefore, in order to increase the number of possible target sites for DSB introduction, it is desirable to have additional CRISPR nucleases with different PAMs in the arsenal. The Cpf1 PAM is 5'-TTTN-3', which expands the range of protospacers that can be used for CRISPR-mediated gene editing. Cpf1 crRNA does not require a tracrRNA and directly functions as a gRNA, which simplifies its use [130]. Cpf1 (Cas12a) also lacks cytotoxicity in several bacteria, and was shown to be effective for genome editing not only in mycobacteria, but in _C. glutamicum_ as well [131].  
Another alternative is the Cas13 effector protein, which is characteristic of class 2 type VI systems, which has properties that are unique among all CRISPR proteins [25]. Among Cas13, four protein families have been identified: Cas13a, Cas13b, Cas13c, and Cas13d [132]. Instead of the usual Cas domains, Cas13 has two domains, one of which is homologous to the eukaryotic, and the other to the prokaryotic nucleotide-binding domain (HEPN). Together they form ribonuclease site [133]. The CRISPR-Cas13 system includes the Cas13 RNA-guided RNase and a short crRNA that recognizes the target RNA sequence.
# RANDnet: Vehicle Re-Identification with Relation Attention and Nuance-Disparity Masks
## 3 Vehicle Re-Identification With RANDnet
### Problem Statement and Intersecting Features  
Vehicle re-identification involves analyzing a given vehicle image and retrieving all corresponding images of the same identity from a gallery. These gallery images are captured by multiple non-overlapping cameras positioned at various viewpoints, emphasizing the need for robust analysis to accurately match vehicle identities across diverse perspectives. The vehicle re-identification problem can be formulated as follows: Within the image set represented as \(<x_{n},y_{i}>^{N,I}\), we aim to establish the correspondence between the vehicle image \(x_{n}\) and its corresponding ID \(y_{i}\). Here, \(N\) and \(I\), respectively, denote the total number of vehicle images and IDs. The common method is to first obtain a feature map \(F\in\mathbb{R}^{C\times H\times W}\)of the query image, then yield the corresponding feature vector by undergoing a global average pooling layer. The similarity of an image pair is measured by calculating the distance of corresponding feature vectors, where the pair with the highest similarity is considered to have the same ID.  
Through the visualization of Figure 2, an intriguing phenomenon was observed: aside from unique features, incorrect IDs can elicit a response in specific parts of the query image. We refer to these features as intersecting features between different IDs.  
In an image pair of entirely different vehicle models, intersecting features might include common background areas, such as roads, trees, road signs, or fences. In a pair with similar car models, intersecting features generally encompass aspects like similar colors, headlights, tires, or emblems. For the pairs with more similarity but not in the same ID, intersecting features often appear in more detailed adormments, like sunroofs. It can be posited that the intersecting features of simple and hard identities do not overlap. In essence, the characteristics of a vehicle can be considered a conglomeration of these intersecting features and the unique features for the ground truth identity. To address these features distinctly, we propose the RANDnet framework with nuance-disparity masks and a relation attention module.
## 3 Adaptive Transmit Diversity Strategy  
In the underwater optical multi-beam NOMA system, it can be seen from Equation (2) that the signal received by the user is affected by intra-beam interference and inter-beam interference in addition to the target signal. Intra-beam interference is caused by the superposition of information from users with lower decoding order than themselves in NOMA multiplexed users. Inter-beam interference results from phenomena such as optical intensity fluctuations, beam spreading, and beam drift caused by underwater turbulence. Additionally, interference from adjacent beams occurs due to the overlap of light spots, leading to undesired signals from non-target beams affecting users.  
In order to effectively mitigate intra-beam and inter-beam interference, we propose an adaptive transmit diversity strategy, and the system block diagram is shown in Figure 5. We assume that the underwater channel state information (CSI) of the multi-beam NOMA system is known at the transmitter. CSI is obtained by training symbols and sent back to the transmitter periodically through uplink. There are several possible ways to realize uplink for UWOC, such as underwater acoustic and visible light with multiple access techniques [24]. The transmitter selects and merges the LED beams based on CSI. The merged LEDs group adopts the diversity technique to convert the inter-beam interference into signal gain. At the same time, it can resist the fading of the signal from turbulence. OFDM-NOMA is employed within the beam to reduce the number of NOMA multiplexed users by pairing groups of users within the cluster. Through the power-domain SC, the superimposed signals are allocated to different subcarriers for transmission. Adopting this strategy can effectively reduce the number of overlapping users on the same time-frequency resources. Subsequently, the modulated signals generated by asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) modulation are emitted by the selected LED. The user receives the signal demodulated by ACO-OFDM and demodulates its signal from the corresponding subcarrier.
# Detecting Deception from Gaze and Speech Using a Multimodal Attention LSTM-Based Framework
###### Abstract
The main reason for this choice is the dramatic improvements achieved by LSTMs in other tasks involving the processing of temporal sequences, such as audio and speech-related applications. Moreover, the incorporation of an attention mechanism in the LSTM framework generally improves the performance of these techniques, as it tries to learn the structure of the temporal sequences by modeling the particular relevance of each frame to the task under consideration [25]. Recently, these models have been successfully utilized for, among others, acoustic event detection [26], acoustic scene classification [27], automatic speech recognition [28], speech emotion recognition [29; 30], cognitive load classification from speech [31; 32] or speech intelligibility level classification [33; 34]. To the best of our knowledge, the use of attentional LSTMs has not been previously explored in the literature neither for ADD systems based on eye-tracker signals nor based on speech signals. In addition, we propose two different alternatives for the fusion of both kind of modalities inside the LSTM architecture, namely Late Fusion and Attention-Pooling Fusion. Finally, we carry out the comparison between the performance of the single-modal and multimodal LSTM-based ADD systems running on frame-level features and the corresponding reference systems based on SVM and compact features as input. This research aims to develop an ADD system working with speech and gaze data collected in real conditions, that is supported on two main hypotheses. The first one states that the dynamics of gaze and speech feature sequences convey important information for deception detection that can be exploited by using attention LSTM networks. The second one refers to the complementarity of the gaze and speech modalities, that can be effectively combined in the LSTM framework.
# FANET Routing Protocol Analysis for Multi-UAV-Based Reconnaissance Mobility Models
## 5 Experimental Results
### RQ2: How Does the Connectivity of Nodes in a Network Affect the Performance of a Protocol?  
We first use a performance metric called network connectivity ratio (NCR) to find out how node connectivity in a network affects protocol performance. We define and use the NCR metric to understand the connectivity of nodes. The underlying assumption of NCR is that the higher the density of FANET nodes, the better the connectivity of the network.  
Figure 2 shows how network connectivity affects routing protocol performance. Given the communication distance of UAV nodes and the topology of the nodes, NCR only indicates the percentage of packets that can be sent from a node to GCS in the network. Therefore, the PDR performance of a routing protocol cannot be higher than the value of NCR. In Figure 2, the center line indicates the maximum performance of PDR. Interestingly, when the NCR value is low, the PDR value is close to the center line. However, when the NCR value becomes higher, the PDR values are farther away from the center line. This means as the NCR value becomes higher, the performance of PDR decreases.  
In Figure 2, AODV, a reactive routing protocol, shows a reasonable PDR performance even when the NCR value is close to 1. The PDR performance of AODV is close to the center line. In contrast, the performance of DSDV, a proactive routing protocol, is stuck between 0.2 and 0.4, even if the NCR value becomes higher.  
To understand the reasons, we marked the NCR values in Figure 3. For example, in Figure 3a, the gray area represents the NCR values. In the figure, as the number of nodes increases, the NCR value increases. If the number of nodes is 30, the NCR value is close to 1.0. It means that almost all nodes are connected. In this case, we expected that most of the packets could be sent from a node to GCS, and the PDR performance would increase. However, in our simulation, we observed that the PDR performance of AODV, OLSR, and DSDV decreased from when the number of nodes was 20, even if the number of nodes increased.  
**Findings.** Even if the connectivity of nodes become higher, the FANET routing protocol performance does not consistently increase. We inferred that it is because the increasing number of nodes causes the overhead of managing a routing table in a protocol.  
Figure 2: Packet delivery ratio per network connectivity ratio.
## 2 Basic Theory
### Product Availability Analysis of Civil Aircraft  
Reliability, maintainability, economy and availability are the four common qualities of civil aircraft. The AFMC, when the formula connects reliability, maintainability and economy, is established in Section 2.1 and has become the one objectives of optimization.  
Furthermore, it is often necessary to set up multiple objectives with interactions for optimizing. The most ideal state for a civil aircraft is to use as little maintenance cost as possible to obtain the best product performance. The paper takes availability into account, connected with reliability and maintainability, which is necessary in order to identify the performance or characterization of products.  
Product availability refers to the probability that the product can work normally at any time when performing the task. According to its physical meaning, if a period of MTBF is taken as a cycle, the availability can be characterized as the ratio of normal working time to MTBF in this cycle. In practical terms, when a product fails, a certain maintenance task will be implemented to restore it to the original. The period of time is mean time to repair (MTTR) or mean shop processing time (MSPT). It should be noted that MSPT usually takes calendar day as a unit, which can be converted into flight hours (FH), consistent with MTBF. Moreover, the planned maintenance tasks, intervals and working hours will be delivered along with the design phase to ensure the operation safety of the aircraft, to ensure that the performance of the products is within a controllable range. Therefore, a certain proportion of time is used to perform the planned maintenance tasks in a cycle. Remove these parts, and the rest is the normal working time of the product in a cycle. The relationship between MTBF, MTTR or MSPT and planned maintenance time is shown in Figure 1.  
Therefore, the formula of product availability is:  
\[\text{A}=\frac{\text{MTBF}-\text{MTTR}-\sum\frac{\text{MTBF}}{\text{MTBM}} \times\text{h}}{\text{MTBF}}\text{ or A}=\frac{\text{MTBF}-\text{MSPT}\times\text{h}_{\text{p}}- \sum\frac{\text{MTBF}}{\text{MTBM}}\times\text{h}}{\text{MTBF}} \tag{3}\]  
Here, h\({}_{\text{p}}\)--the average daily service time of the product to facilitate the conversion of units.
# Capturing Features and Performing Human Detection from Human Gaits Using RFID
## 3 Results
### Experimental Setup  
Experimental environment: Figure 10 shows our experimental scene, where the tag group is attached to the wall on one side of the corridor, and the reader is positioned on the wall on the other side and perpendicular to the middle tag. The direct tag spacing is 0.7 m, and the vertical distance between the reader and the tag is 2 m. When conducting person detection, the user starts to walk through the corridor in the reader's receiving range. When the reader receives the signal of the tag, it transmits the tag to the PC through Ethernet for data reception and identification.  
Hardware environment: As shown in Figure 11, the hardware facility contains an Impinj Speedway R420 reader and an RFID UHF circularly polarized antenna (9 dBi gain); the receiving range is 120\({}^{\circ}\)). The operating frequency of the reader is 920.875 MHz, the tag uses a 73 \(\times\) 20 mm UHF flexible anti-metal UR107 tag and a laptop computer is used for data reception.  
Figure 10: Experimental scene.  
Software Facilities: We run the model on a Lenovo computer equipped with 2.5 GHz AMDR7 and 16 gigabytes of memory for data acquisition and preprocessing, the RFID reader is connected to the laptop via an Ethernet cable, and the low-level reader protocol (LLRP) is used for communication. The method is implemented in C, and the neural model we designed is implemented in Python.  
Dataset: We collected a total of 7500 data samples from 10 volunteers (all 10 volunteers were adults, four women and six men, and two of the four women were the same height, two were different in height, and six men were different in height). To evaluate the effectiveness of the person detection, volunteers were asked to walk in a normal walking manner, and each group of volunteers crossed the reader reception area for about 3-6 s, and then the next group of volunteers continued sampling.
## 4 Proposed Scheme
### Phase 2: Sensing and Reporting
#### 4.2.2 Watermarking Algorithm  
The primary goal of the proposed watermarking algorithm is to randomly embed the watermark within the data and still manage to extract it. Also, RWFS strives to resolve and effectively maintain the energy level of the nodes. The algorithm consists of two procedures: watermark generation and watermark embedding.  
The watermark generation process takes sensory data after encryption \(E_{i}\) as input from the sensor node \(S_{i}\) and generates a watermark \(W_{i}\). The time and sensor \(ID\) are also used as inputs to a keyed-hash message authentication code (HMAC) to produce the final watermark. The final watermark is calculated as:  
\[W_{i}\ =Hash\big{(}(E_{i}||\ Time\ ||\ S_{i}ID),k_{cj}\big{)} \tag{6}\]  
After that, the watermark is embedded at random redundant spaces of the encrypted data \(E_{i}\) as shown in Figure 4. These redundant spaces are determined by using a pseudo-random number generator.  
There are many ways to build pseudo-random generators (PRNG) suitable for use with our system. For simplicity, we take the linear congruential generator (LCG) [43] in the following equation to compute each position of \(W_{i}\)  
\[X_{n+1}=\left(a_{j}X_{n}+c_{j}\right)mod\ m_{j}. \tag{7}\]  
Here, \(m_{j}>0\) is a modulus, \(a_{j}\)\((0<a_{j}<m_{j})\) is the multiplier, \(c_{j}\)\(\left(0\leq c_{j}<m_{j}\right)\) is the increment, and \(X_{n}\)\(\left(0\leq X_{n}<m_{j}\right)\) is the current seed. Each generated \(X_{n+1}\) is used as a pseudo-random number and becomes the new seed. Each node computes three values of \(X_{n+1}\) to use as the positions of \(W_{i}\).  
To avoid every cluster having the same number generated for it, each cluster \(j\) has different parameters for its pseudo-random number generator.  
The sensor's modules responsible for the two sub procedures of data encryption and watermarking generation are shown in Figure 5.  
Figure 4: Watermark embedding.  
Figure 5: A block diagram of the Randomized Watermarking Filtering Scheme (RWFS) showing the modules of the sensor node and cluster head. It also shows the flow of data between the modules.