esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Short-Time Fourier Transformation  
We conducted a short-time Fourier transform (STFT) analysis to investigate the time-varying frequency characteristics and power distribution of EEG data. A notable advantage of utilizing STFT is its suitability for analyzing non-stationary EEG signals [64]. Unlike conventional Fourier analysis methods, which assume signal stationarity throughout, STFT breaks down the signal into smaller, overlapping windows. This approach is particularly beneficial for EEG data, as it accommodates the dynamic nature of brain activity, which can vary significantly over time due to shifts in cognitive states.  
The equation for the short-time Fourier transform (STFT) is as follows:  
\[x(\tau,f)=\int_{-\infty}^{\infty}x(t)w(t-\tau)e^{-l2\pi ft}dt \tag{1}\]  
where \(x(\tau,f)\) is the STFT of the signal \(x(t)\) at time \(\tau\) and frequency \(f\).  
\(x(t)\) is the input signal.  
\(w(t)\) is the window function.  
\(\tau\) represents time.  
\(f\) represents time.  
Initially, the raw EEG data of subject 1 for both low and high workload modes were acquired, and all 14 channels were selected for subsequent analysis. With a sampling frequency of 128 Hz, the data were processed to capture frequency bands with the frequency ranges mentioned in Table 2.  
The resulting visualizations depicted the power spectrum distribution across different frequency bands for both low mode and high mode conditions. Specifically, Figure 6a,b illustrate the frequency band visualization of subject 1 in the both low mode and high mode.  
Figure 5: EEG channels correlation heat map (high mode).
## 1 Introduction  
For traditional airborne photogrammetry with unmanned aerial vehicles (UAVs), camera poses (position and attitude) are determined indirectly using the well-known method bundle block adjustment (BBA). The BBA method is effective and widely employed for photogrammetric production when the scene is well-textured and allows for automated tie point extraction.  
The photogrammetric accuracy is strongly dependent on the acquisition geometry and the number of ground control points (GCPs) as well as their distribution within the image block [1; 2]. Among different configurations of UAV acquisitions, the corridor mapping is of particular interest, for instance in dike surveillance, highway planning and power line surveys [3]. Nevertheless, it is challenging in many aspects. The challenges come mainly from the special network geometry, and the fact that a well-distributed GCP network is not easy to ensure. The acquired images are often in strips, which makes it difficult to have satisfying cross-track overlaps and thus results in a less accurate camera pose determination. While the employment of a large number of GCPs prevents stereo model distortion [4], the field work of GCP establishment can be substantially expensive and time-consuming.  
With the appearance and development of global navigation satellite system (GNSS), it is possible to know the accurate camera projection center positions at exposure and thus to reduce, or even eliminate, the requirement of GCPs within the image block [5; 6]. Several GCPs are still used for the purpose of improving the redundancy and identifying possible bias in GNSS positioning [7; 8]. When coupling this GNSS-assisted bundle block adjustment approach to an inertial navigation system (INS), camera poses can be determined directly for each image without the need of traditional BBA procedure. This said, for small UAVs, an accurate position and attitude aerial control remains challenging due to the limited availability of payload, power and volume.
# NRNH-AR: A Small Robotic Agent Using Tri-Fold Learning for Navigation and Obstacle Avoidance
## 2 Materials and Methods
### Overview of the NRNH-AR Algorithm  
The overview of the NRNH-AR algorithm is shown in Figure 1. The robot agent uses two sensors, a camera and an ultrasonic sensor, HC-SR04, collocated in front of the robot agent, as shown in Figure 2. While the robot agent does not find any obstacle ahead, it realizes an action randomly selected within (forward, right turn, left turn, back). The agent halts if the ultrasonic sensor emits an active signal, indicating an obstacle to its front. Then the agent captures three images with the camera at angles of 15\({}^{\circ}\), 90\({}^{\circ}\), and 165\({}^{\circ}\), as shown in Figure 2, considering that 90\({}^{\circ}\) is the robot looking straight ahead. These three images are introduced sequentially to pre-trained CNN 1, to determine whether one of the three images is the target object. If yes, the agent achieves its goal; otherwise, the robot agent realizes the obstacle avoidance action using the three previous images, concatenating them to form a single image, called a Concatenated Observation (O_co), which is used as an input for a second pre-trained CNN (CNN 2) to understand the collision state and train the actor and critic networks that predict the best action with the highest quality in the current state of the agent.  
To optimize the use of the limited resources of the small robot agent, we have structured the training process into three distinct phases: SSL, USL, and DRL. The object-tive of all learning phases is to train two networks, which are a predicted actor network and a predicted critic network. These two networks are fully connected neural networks with two hidden layers. Three learning phases share the networks' connection weights. Instead of training these networks directly using one of the DRL algorithms, such as DQN and TD3, we use triple learning phases composed of SSL, USL, and DRL to reduce computational costs and required memory space.  
In the final DRL phase, the agent builds upon the knowledge acquired in the previous two phases, SSL and USL, through the updated connection weights of networks, and introduces the concepts of rewards. The agent also considers the time it takes to achieve its goal. Therefore, each time step until the agent finds the object causes a negative reward. Through direct interaction with the environment, the agent learns and adjusts its actions based on the rewards received. This continuous learning process causes the agent's behavior to improve and adapt over time.
# Autocorrelation Matrix Knowledge Distillation: A Task-Specific Distillation Method for BERT Models
## 4 Experiments
### Training Details and Baselines  
In our experiment, we used a BERTBase model fine-tuned for specific tasks as the teacher model [2]. The BERTBase model has 12 layers (\(M=12\)), a hidden size of 768 (\(D=768\)), a feed-forward size of 3072 (\(D_{i}=3072\)), and 12 attention heads (\(h=12\)), totaling 109 million parameters. The student model was a pre-trained language model, BERTTiny, with a smaller parameter size. The BERTTiny model has 4 layers (\(N=4\)), a hidden size of 312 (\(D^{\prime}=312\)), a feed-forward size of 1200 (\(D^{\prime}_{i}=1200\)), and 12 attention heads (\(h=12\)), totaling 14.5 million parameters.  
The distillation temperature \(t\) was chosen from the set {1, 4, 8}, while the value of \(\alpha\) was chosen from {0.2, 0.5, 0.8, 1}. The learning rate was chosen from {\(1\times 10^{-5},2\times 10^{-5},5\times 10^{-5}\)}, and the batch size was chosen from [16; 32]. For the fine-tuning task, the number of epochs was set to 5 without data augmentation, whereas the distillation task was conducted over 25 epochs with data augmentation. The sequence length for task-specific distillation was consistently set to 128. During the skip learning process, we set \(k=3\), meaning that BERTTiny learned from BERTBase at every third layer. The best results were selected from each experiment.  
We compared our method, BERTTiny-AMKD, with several baseline methods, including BERT-KD [14], which represents the traditional prediction layer distillation method, BERTPKD, and DistilBERT. We fine-tuned the published pre-trained models BERT4-PKD [20] and DistilBERT4[19] on each specific task using the recommended hyperparameters. Table 1 provides a structural comparison of the models used in our experiments. BERTSmall is one of the 24 smaller BERT models available from the official Google repository [41]. Table 2 offers a detailed comparison of the distillation positions employed by the baseline methods and our proposed AMKD method. Our AMKD approach enables the student to more comprehensively learn knowledge from the teacher model.
###### Abstract  
Aspect-Based Sentiment Analysis (ABSA) is a crucial process for assessing customer feedback and gauging satisfaction with products or services. It typically consists of three stages: Aspect Term Extraction (ATE), Aspect Categorization Extraction (ACE), and Sentiment Analysis (SA). Various techniques have been proposed for ATE, including unsupervised, supervised, and hybrid methods. However, many studies face challenges in detecting aspect terms due to reliance on training data, which may not cover all multiple aspect terms and relate semantic aspect terms effectively. This study presents a knowledge-driven approach to automatic semantic aspect term extraction from customer feedback using Linked Open Data (LOD) to enrich aspect extraction outcomes in the training dataset. Additionally, it utilizes the N-gram model to capture complex text patterns and relationships, facilitating accurate classification and analysis of multiple-word terms for each aspect. To assess the effectiveness of the proposed model, experiments were conducted on three benchmark datasets: SemEval 2014, 2015, and 2016. Comparative evaluations with contemporary unsupervised, supervised, and hybrid methods on these datasets yielded F-measures of 0.80, 0.76, and 0.77, respectively.  
aspect term extraction; linked open data; knowledge-driven approach Article
###### Abstract  
The statistical machine translation for the Arabic language integrates external linguistic resources such as part-of-speech tags. The current research presents a Bidirectional Long Short-Term Memory (Bi-LSTM)--Conditional Random Fields (CRF) segment-level Arabic Dialect POS tagger model, which will be integrated into the Multitask Neural Machine Translation (NMT) model. The proposed solution for NMT is based on the recurrent neural network encoder-decoder NMT model that has been introduced recently. The study has proposed and developed a unified Multitask NMT model that shares an encoder between the two tasks; Arabic Dialect (AD) to Modern Standard Arabic (MSA) translation task and the segment-level POS tagging tasks. A shared layer and an invariant layer are shared between the translation tasks. By training translation tasks and POS tagging task alternately, the proposed model can leverage the characteristic information and improve the translation quality from Arabic dialects to Modern Standard Arabic. The experiments are conducted from Levantine Arabic (LA) to MSA and Maghrebi Arabic (MA) to MSA translation tasks. As an additional linguistic resource, the segment-level part-of-speech tags for Arabic dialects were also exploited. Experiments suggest that translation quality and the performance of POS tagger were improved with the implementation of multitask learning approach.  
Keywords:NMT; MTL; POS tagging; CRF; Bi-LSTM; Arabic dialects; encoder; decoder; MSA _Article_
# Integration of _Salmonella_ into Combination Cancer Therapy
## 4 Clinical Application of _Salmonella_ in Combination Cancer Therapy
### Efforts to Overcome Challenges
SipA protein induces caspase-3-mediated cleavage of the multi-drug resistance p-gp and inhibits its function. Despite the observation that the SipA-AuNPs nanoparticle did not result in any therapeutic effects when tested in murine tumor models of colon and breast cancers, the enhanced anti-tumor efficacy was observed when combined with doxorubicin. The therapeutic benefit was delivered by the capacity of the SipA protein to abrogate the function of p-gp and therefore retrieve the desired effect of doxorubicin. The concept of applying nanoparticles to deliver _Salmonella_ proteins is well-accepted in circumventing the safety concern of using infectious agent like _Salmonella_, but nanoparticles lack the unique feature of _Salmonella_ in the preferential colonization of tumor tissues. A recently published paper utilized nanoparticle technology and _Salmonella_ to enhance the treatment efficacy and safety [194]. They employed silver nanoparticles (AgNPs) conjugated with sialic acid to locally deplete tumor-infiltrating neutrophils through the selective recognition of L-selectin, thereby enhancing the efficacy of _Salmonella_. AgNPs also exert their function through inducing direct tumor cell killing and clearing _Salmonella_ following tumor eradication to minimize the undesirable side effects. This way, the combination of _Salmonella_-AgNPs resulted in a superior therapeutic outcome. Furthermore, controling gene expression,either remotely or through the incorporation of gene promoters responsive to tumor-associated antigens, has shown a good potential in minimizing toxicity to normal tissues (discussed in Section 2.5). In combinatorial therapies that involve bacterial treatment, high doses of bacteria are not necessarily correlated with a better outcome. Bacteria can be utilized at a low dose that is capable to induce changes in the immune components of the tumor microenvironment without any significant tumor inhibition effect. This way, it may enhance the therapeutic outcome and increase the efficacy of other treatments while keeping toxicity to minimal. As previously mentioned, the therapeutic index of bacterial therapy in pre-clinical model has been enhanced over the past few years with the designation of engineered bacteria. While retaining tumor specificity and anti-tumor efficacy, bacteria have been engineered for the purpose of (a) improving safety, (b) enhancing tumor targeting and minimizing toxicity to normal tissues, and (c) delivering various anti-tumor therapeutic agents such as cytokines, cytotoxic agents, tumor-associated antigens, and prodrug enzymes [21]. Taken all together, the durable success achieved with combination therapies (as compared to monotherapies) in pre-clinical models has increased the demand to overcome the challenges associated with bacterial therapy. This, in turn, will pave the way for implementing combination therapies involving _Salmonella_ in clinical use.
## 4 Results and Discussion
### Static Workbench Experiment

Table 3: Effect of rotational speed on seed metering performance. Figure 7: Seeding performance. (**a**) Total seeding rates and variation coefficients with respect to the rotational speed of the gear disc; (**b**) Comparison of seeding rates of each port; (**c**) Variations of seeding rates of each port with respect to the rotational speed of the gear disc. It can be seen from Table 3 and Figure 7 that:  
1. The maximum average total seeding rate was 537.17 g/min when the rotational speed of the gear disc was 45 r/min, equivalent to 8.95 g/s, which indicated that the agronomic requirement of quantity of seeds per unit area could be met when the flying speed of the aircraft is 7.11 m/s theoretically. 2. Both the stability variation coefficient of the total seeding rate and the consistency variation coefficient of the seeding rate between each port were very low, with the maximum values being 2.37% and 4.89%, respectively. This means that the system can ensure stability and uniformity in both the total seeding rate and the seeding rate of individual ports. 3. The total seeding rate \(Y\) (g/min) increased with the rotational speed of the gear disc \(X\) (r/min) according to a polynomial fitting \(Y=0.46014X^{2}-20.7614X+544.78086\) with R\({}^{2}=0.9934\).
## 2 Methodologies
### Improve the Training of BiGAN  
#### 2.3.1 Wasserstein GAN with Gradient Penalty  
Although the origin GAN model is simple to construct, a high-quality GAN model is hard to obtain. However, the training of the origin GAN may have problems such as non-convergence and mode collapse. BiGAN has the same problems because it uses similar loss functions [20]. Those issues reduce the usability, that the model not being able to generate samples of all fault types. Therefore, it is necessary to improve the training process of BiGAN.  
The inappropriate measurement method causes the difficulty of training [21]. The original GAN optimizes the Jensen-Shannon (JS) divergence between the generated and real distribution. However, when the two distributions do not have any non-negligible intersection, the JS divergence is a constant, which causes the vanishing gradient.  
To solve this problem, a new measurement method is proposed, namely Earth Mover distance or Wasserstein distance [22]. Even if the two distributions do not have any overlap, this metric can still indicate the distance between them and provide effective gradients for training. The model built based on Wasserstein distance is called Wasserstein GAN (WGAN). The value function of WGAN is:  
\[\min_{G}\max_{D}V(D,G)=\mathbb{E}_{x\sim p_{X}}[D(x)]-\mathbb{E}_{z\sim p_{Z} }[D(G(z))] \tag{8}\]  
which is similar to Equation (1) but without taking the logarithm, and alternative \(G\) loss is also taken.  
Similarly, we can derive the loss function from Equation (8):  
\[\mathcal{L}_{D}=-\mathbb{E}_{x\sim p_{X}}[D(x)]+\mathbb{E}_{z\sim p_{Z}}[D(G(z ))] \tag{9}\]  
\[\mathcal{L}_{G}=-\mathbb{E}_{z\sim p_{Z}}[D(G(z))] \tag{10}\]  
The Equation (9) is also the definition of Wasserstein distance.  
In addition to the loss function, the main structural modification of WGAN is about the discriminator \(D\). It removes the sigmoid activation function at the last layer so that the output is no longer limited to (0,1). However, to enforce the Lipschitz constraint, WGAN demands clipping the weight of \(D\) with a constant \(c\) after every update step, i.e., \(w_{D}\in[-c,c]\). This procedure may cause a phenomenon that the value of \(w_{D}\) always approximates \(c\) or \(-c\). If \(c\) is too large or too small, it may lead to vanishing or exploding gradients.  
Therefore, [PERSON] et al. [23] improved WGAN by adding a gradient penalty term in Equation (9), called WGAN-GP. It can avoid weight clipping and enforce the Lipschitz constraint. The loss function of the WGAN-GP's discriminator is:  
\[\mathcal{L}_{D}= -\mathbb{E}_{x\sim p_{X}}[D(x)]+\mathbb{E}_{z\sim p_{Z}}[D(G(z))] \tag{11}\] \[+\lambda\mathbb{E}_{\hat{x}\sim p_{\hat{x}}}[(||\
abla_{\hat{x}} D(\hat{x})||_{2}-1)^{2}]\]  
where the additional part compared to Equation (9) is the gradient penalty term. \(\hat{x}\) is the linear interpolation between the real data \(x\) and the generated data \(G(z)\):  
\[\hat{x}=\epsilon x+(1-\epsilon)G(z) \tag{12}\]  
where \(\lambda\) is penalty coefficient, usually take \(\lambda=10\), and \(\epsilon\) is a random number \(\epsilon\sim U[0,1]\).
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 2 Synthesis Methods
### Chemical Methods
#### 2.2.4 Polyol Method  
The polyol method stands out as one of the simplest approaches for nanoparticle synthesis. This technique facilitates the synthesis of nanoparticles from inorganic compounds, including alloys, sulfides, oxides, fluorides, etc., [50]. It presents significant advantages by enabling the cost-effective single-step production of high-crystalline hydrophilic MNPs that can be easily scaled up [51].  
In this method, polyols serve multiple roles, acting as a polar organic solvent for metal precursors, reducing agent, and, in certain instances, complexation agent for metal cations [20]. The polyol process has demonstrated its ability to control MNPs' morphologies, ranging from single-core spherical MNPs to more intricate multicore nanostructures, including hollow spheres and nanoflowers [51]. Additionally, adjusting the reaction temperature can increase NP size [52].  
This technique possesses several advantages, including the elimination of separate calcination steps and the use of polyol as the sole solvent [53]. Furthermore, polyols are recognized as cost-effective and environmentally friendly solvents, widely employed in various industries [53].
## 5 Performance Analysis  
### Simulation Environment  
Performance analysis to evaluate the performance of the proposed protocols was done using MATLAB simulation environment. A WSN domain of size \(2000\times 2000\) m\({}^{2}\) was simulated. For good coverage in the network, 2500 nodes were randomly distributed. Sink node was the destination for all the packet transmissions. The location of the sink node was assumed at the center of the network. The sensor node sensing range was set to 30 m to guarantee multi-hop communications between source nodes and sink node. The k-nearest neighbors tracking approach was employed to monitor the network domain. The network model is explained in details in Section 3 above. A total of five protocols were included in the analysis: the ProbR, PhaP, TreeR, PhaT, and the phantom single-path routing (Pha). The Pha protocol was included in the analysis as a representative protocol for the traditional SLP routing protocols, for comparative analysis. The network configuration for the PhaP protocol was done according to Figure 1. The network parameters were configured as follows: \(dP_{m}=400\) m, \(dP_{out}=600\) m, and \(T_{P}=0.5\). For PhaT protocol, \(d_{NB}=200\) m, \(d_{H}=3\) hops, \(d_{P}=4\) hops, and \(T=0.5\). A cautious adversary was deployed with initial location around the sink node to ensure maximum probability of packet capture. Adversary detection range was set to 30 m similar to sensor node sensing range to guarantee that the adversary performs hop-by-hop back tracing attack. The waiting timer for the adversary was set to four source packets. The adversary model is explained in details in Section 3 above. Only real packets were transmitted in the PhaP and PhaT protocols. In the ProbR and TreeR, real packets and fake source packets were transmitted simultaneously. The simulation was run for 500 iterations and average values were considered. The network simulation parameters are summarized in Table 6. The performance metrics used in the analysis are explained in the next sub-section.