esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Neural Networks Based on Mutual Information via Information Geometry
## 2 Materials and Methods
The test accuracy on the Iris dataset for NN, DBN, AMSA, MI-NN, and MI-DBN (\"MI-\" denotes the proposed methods) can be seen in \[p(r|x)=p(\overline{y},z|x,u)=p(\overline{y}|z,v)p(z|x,w) \tag{1}\]8 The network structures used in this experiment are 4-3-3 and 4-6-7-3, which correspond to the shallow networks (i.e., NN and MI-NN) and the deep networks (i.e., DBN, AMSA, and MI-DBN), respectively. The activation and output function is sigmoid. \[p(r|x)=p(\overline{y},z|x,u)=p(\overline{y}|z,v)p(z|x,w) \tag{1}\]9 The tests of NN, MI-NN, DBN, AMSA, and MI-DBN with Iris. The accuracies are plotted on the horizontal axis. (**a**) \(\alpha=10\%\); (**b**) \(\alpha=30\%\); (**c**) \(\alpha=50\%\). The test accuracies on the Poker Hand dataset for NN, DBN, AMSA, MI-NN, and MI-DBN (\"MI-\" denotes the proposed methods) can be seen in \[J=E_{x}[(\overline{y}-y)^{2}]+\lambda I(x;r) \tag{2}\]0 The network structures used in this experiment are 85-47-10 and 85-67-48-10, which correspond to the shallow networks (i.e., NN and MI-NN) and the other networks (i.e., DBN, AMSA, and MI-DBN), respectively. The activation and output function is sigmoid. It can be seen that the proposed algorithm outperforms the previous methods on both datasets successfully, although a sole exception is that the MI-NN is slightly worse than AMSA with Iris when \(\alpha=10\%\). This finding means that the proposed algorithm can be applied to the problem of  
\[J=E_{x}[(\overline{y}-y)^{2}]+\lambda I(x;r) \tag{2}\]1 The tests of NN, MI-NN, DBN, AMSA, and MI-DBN with Poker Hand. The accuracies are plotted on the horizontal axis. (**a**) \(\alpha=10\%\); (**b**) \(\alpha=30\%\); (**c**) \(\alpha=50\%\). classification. In addition, the proposed algorithm improves the traditional algorithms constantly, when the percent of labeled training samples changes. To study the correlation between the performance and \(\alpha\), we illustrate the difference between the two methods with varied \(\alpha\) in \[J=E_{x}[(\overline{y}-y)^{2}]+\lambda I(x;r) \tag{2}\]2 According to Figure 4, there is a positive correlation between the improvement brought by the proposed method and the value of \(\alpha\). The reason is that more label data leads the proposed method to absorb more useful information for classification. In contrast, this aspect does not hold true for the other algorithms.
# Streetlight Control System Based on Wireless Communication over DALI Protocol
## 4 Implementation of the Wireless Node
### Software Node
Because of this, some encrypted frames using a SOC CC2530 security coprocessor with 128-bit AES encryption and pre-shared key authentication have been used.
## 5 Numerical Simulations
### String Stability Verification
\begin{array}{ll}1\;\mathrm{m}/\mathrm{s}^{2},&20\;\mathrm{s }\leq t\leq 23\;\mathrm{s}\\ -2\;\mathrm{m}/\mathrm{s}^{2},&80\;\mathrm{s}\leq t\leq 83\;\mathrm{s}\\ 0,&\mathrm{otherwise}.\end{array} \right. \tag{40}\]  
It is obvious that the acceleration and deceleration of the leader vehicle in this scenario are the low-frequency perturbations. Thus, the time headway is set as \(h=0.7746\;\mathrm{s}>h_{\mathrm{L,min}}\), which is available to guarantee the string stability. To demonstrate the practical significance of our proposed results for the vehicular platoon control, an additional simulation is performed under the above three cases from the perspective of the tracking performance and ride comfort, where the string stability conditions are satisfied. The definitions of the above performances are designed according to [37]. Tracking performance:  
\[J_{Ti}=\omega_{e}\sum_{t=0}^{\infty}e_{i}^{2}(t)+\omega_{v}\sum_{t=0}^{\infty} (v_{i}(t)-v_{i-1}(t))^{2} \tag{41}\]  
Ride comfort:  
\[J_{Ci}=\omega_{c}\sum_{t=0}^{\infty}\left(\dot{a}_{i}(t)\right)^{2} \tag{42}\]  
where \(\omega_{e}=0.01\), \(\omega_{v}=0.01\), and \(\omega_{c}=0.001\) are the weighting parameters of the spacing error, relative velocity, and derivative of acceleration, respectively. Table 3 shows the simulation results of the above three cases. For the tracking performance, the values under the three cases decrease with the vehicle index, illustrating that the spacing errors and relative velocity errors decrease upstream, which also states that the vehicular platoon is string-stable. Then, one can see that the values of \(J_{Ti}\) and \(J_{Ci}\) of Case 2 are smaller than that of Case 1. This means that when subjected to the sinusoidal  
Figure 8: Performance of the vehicular platoon influenced by low–frequency perturbations with \(h_{\mathrm{L,min}}<h<h_{\mathrm{A,min}}\): (**a**) Spacing error. (**b**) Velocity. Figure 9: Performance of the vehicular platoon influenced by low–frequency perturbations with \(h<h_{\mathrm{L,min}}\): (**a**) Spacing error. (**b**) Velocity. perturbations, the vehicular platoon for low frequency has better properties in the tracking performance and ride comfort than that for high frequency. Moreover, the values of \(J_{Ti}\) and \(J_{Ci}\) of Case 2 are smaller than those of Case 3. This means that when subjected to low frequency, the vehicular platoon under the sinusoidal perturbations exhibits better performance indicators than that in the \"acceleration-cruise-brake\" scenarios. The above statistical evaluation is conducted on the vehicular platoon that satisfies the string stability conditions in the three different scenarios, and indicates the practical significance of our proposed results for the vehicular platoon control.
# Characterization of Nutrient Disorders of _Camabis sativa_
###### Abstract  
Essential plant nutrients are needed at crop-specific concentrations to obtain optimum growth or yield. Plant tissue (foliar) analysis is the standard method for measuring those levels in crops. Symptoms of nutrient deficiency occur when those tissue concentrations fall to a level where growth or yield is negatively impacted and can serve as a visual diagnostic tool for growers and researchers. Both nutrient deficiency symptoms and their corresponding plant tissue concentrations have not been established for cannabis. To establish nutrient concentrations when deficiency or toxicity symptoms are expressed, _Cannabis sativa_ 'T1' plants were grown in silica sand culture, and control plants received a complete modified Hoagland's all-nitrate solution, whereas nutrient-deficient treatments were induced with a complete nutrient formula withholding a single nutrient. Toxicity treatments were induced by increasing the element tenfold higher than the complete nutrient formula. Plants were monitored daily and, once symptoms manifested, plant tissue analysis of all essential elements was performed by most recent mature leaf (MRML) tissue analysis, and descriptions and photographs of nutrient disorder symptomatology were taken. Symptoms and progressions were tracked through initial, intermediate, and advanced stages. Information in this study can be used to diagnose nutrient disorders in _Cannabis sativa_.  
macronutrients; micronutrients; cannabis; deficiency; toxicity; fertility; symptomatology; hemp; diagnostics; plant tissue analysis; CBD; THC; foliar
# Edge-Aided Reliable Data Transmission for Heterogeneous Edge-IoT Sensor Networks
## 2 Related Works
### Reliable Data Transmissions in Sensor Networks  
Reliable data transmission is a well-studied problem in sensor networks. To deal with possible packet losses, there are basically two solutions: Reliable routing and error recovery. In reliable routing, each node selects the most reliable links for data transmission such that potential interference can be avoided [4]. [PERSON] et al. [24] propose a typical routing protocol in ad-hoc networks which selects the node with smallest expected number of transmissions (ETX) as the next-hop forwarder for each hop. ECD [25] additionally considers different link features such as link quality and link correlation to improve the transmission efficiency and reliability. CD [26] is an efficient dissemination scheme (wireless multicast) to improve the reliability and reduce propagation delay of data transmissions. CoCo and CoCo+ [27; 28] establish a fixed network structure for efficient multicast in sensor networks, where link features and transmission progress are both considered. In error recovery, some works use retransmissions to deal with packet losses and some other works employ coding schemes such as network coding and forward error correction codes for reliable data transmissions. Seda [20] is a partial retransmission scheme to save the transmission cost in sensor networks. ULTRA [29] is an out-of-order data transmission scheme for bulk data transfer, where all packets can be delivered to the destination nodes without following strict sequences. TinyRS [21] employs FEC codes to improve the packet reception rate for wireless transmissions. ZiXOR [22] is a lightweight coding scheme to reduce the coding delay and improve the reliability of data transmissions. These works do not rely on extra resources. However, in edge computing for IoT (edge-IoT) networks, edge servers are deployed throughout the network and can be used as potential senders to forward and retransmit data packets. As a result, in such a new network paradigm, the existing works on data retransmissions and error correction schemes cannot be directly employed.
# Quantum Key Distribution with Post-Processing Driven by Physical Unclonable Functions
## 1 Introduction
A PUF operates as a pseudo-random number generator (PRNG) in the sense that it produces a random numerical key as a response to an input stimulus (to be referred to hereafter as a challenge). The nature of the challenge depends on the PUF under consideration, while the response to a given challenge depends strongly on the internal disorder of the device. The raw noisy response is classically processed by means of a fuzzy extractor to yield a nearly perfect robust binary key. The fuzzy extractor typically involves two separate processes: reconciliation and hashing. The former aims at a reconciled key that is not affected by environmental variations and ageing of the token, whereas the latter compresses the reconciled key further, so that the final key is nearly uniformly distributed. A useful PUF is robust, easy to evaluate, difficult to replicate, unique (highly unlikely for two independent devices to return the same key for the same challenge), and unpredictable (very difficult or impossible to predict the response to a given challenge). Many of the PUFs that have been discussed in the literature satisfy these requirements. The main advantage of PUFs is that they remove the necessity of secret key storage, as the key is essentially physically stored in the disordered token and can be recovered on demand when the appropriate challenge is given. The only requirement is that the user has access to the token. The strength of a PUF is generally determined by the number of supported challenge-response pairs (CRPs), or to be more precise, by the way the number of potential CRPs scales with the increasing token size. In general, a PUF for which the number of supported CRPs grows exponentially with the size of the token is considered to be strong, while linear or polynomial growth typically refers to weak PUFs. Given that weak PUFs support a relatively small number of CRPs, an attacker can obtain the responses to all possible challenges, provided they have access to the PUF for a sufficiently long period of time. Hence, they can emulate a particular PUF in any cryptographic protocol without having an actual clone of the token. By contrast, strong PUFs support a much larger set of CRPs and an attacker must have access to the PUF for a time period that is considerably longer than the one required for the readout of a weak PUF. Ring-oscill  
Figure 3: Schematic representation of a physical unclonable function (PUF). The token (sometimes also referred to as PUF tag), is a device with internal physical disorder. The internal disorder of the token is imprinted into its response to a physical challenge. The raw response is processed classically in order to yield a nearly perfect and robust random key. considered to be weak, whereas optical and arbiter PUFs (a type of delay-based silicon PUF) are considered to be strong [26; 27; 28; 29; 30; 31; 32; 33].
# Segmentation of Rice Seedings Using the YCrCb Color Space and an Improved Otsu Method
## 1 Introduction
Often, the color-index-based methods cannot even accurately separate plants from the background without an adaptive threshold. Furthermore, they have failed to segment plant pixels from the background in low- or bright-light conditions [16]. To obtain a good segmentation result, more learning-based approaches have been proposed such as PSO-MM (partial swarm optimization clustering and morphology modeling) [17], decision trees [18], and support vector machines [19]. These methods are carried out through supervised and unsupervised learning processes, where the training and classification phases are generally offline and online processes, respectively. Although they can obtain good crop segmentation results in a complex environment, they require sufficient training samples and additional computational costs for training and may therefore not be suitable for real applications. Compared with learning-based methods, color-index-based approaches have some advantages. Firstly, the color is an intuitionistic and easy characteristic to distinguish plants from the background. Secondly, the color-index-based method is simple and takes a relatively small amount of calculation time and achieves satisfactory segmentation results with a dynamic threshold. Additionally, to the best of our knowledge, there are very few studies aiming at handling segmentation of rice seedlings in paddy fields. In order to accurately segment rice seedlings in a complex field environment, a color-index-based segmentation method was proposed in this paper. Firstly, the method selects the YCrCb color space in the image graying process, due to illumination invariance of Cb and Cr components. Given that the green component value of rice seedling is larger than that of the background, the Cg component is built, and 2 Cg-Cb-Cr is constructed as the color-index used in this study. Subsequently, the threshold segmentation process is performed based on an improved Otsu method which weights the background variance of the traditional Otsu method. The weight parameter is adaptively adjusted with the cumulative probability of rice seedlings' occurrence to obtain a satisfactory segmentation in this research. Additionally, the threshold is searched within a limited range to reduce the computation time. Experiments on several test rice seedling images in a paddy field were conducted to demonstrate the effectiveness of the proposed method. The rest of the paper is organized as follows. Section 2 describes the details of materials and methods. Experimental results of our proposed method are presented in Section 3. The conclusions are shown in Section 4.
# Gaze Estimation Based on a Multi-Stream Adaptive Feature Fusion Network
## 3 Proposed Method  
### Network Overview  
The MSMI-Net architecture, as shown in Figure 1, comprises two independent streams designed to enhance gaze estimation performance by comprehensively processing eye and face images. The eye image stream uses left and right eye images as input, while the fusion stream combines the left and right eye images with the full face image, totaling five input channels. To enhance the comparison with benchmark models and adapt to the smaller eye image size in the dataset, all inputs are cropped to 96 \(\times\) 96. In the eye image stream, the input images first pass through a convolutional layer with a kernel size of 4 and a stride of 2, followed by normalization. The images then enter a ConvNext module [36]consisting of four stages, where the ConvNext Block is configured as [3; 3; 27; 3], with the channel dimension gradually expanded from 128 to 1024. Downsampling operations are applied before each stage to reduce the feature map size, thereby constructing a deep CNN specifically designed for high-dimensional feature extraction from the eye region. Meanwhile, the fusion stream adopts a lightweight convolutional structure to efficiently extract low-dimensional features from both the eyes and the face, while avoiding excessive computational overhead. This stream utilizes a 5-layer 3 \(\times\) 3 convolutional network to gradually increase the feature channel dimension from 3 to 256, and introduces residual connections during feature extraction to preserve the original eye feature information, thereby enhancing the impact of local features on gaze estimation. Additionally, two max-pooling operations are incorporated into the five convolutional layers to adapt to input image size variations while improving the model's robustness to features at different scales. Given the varying importance of eye and facial images in gaze estimation tasks, MSMI-Net designs an AWAM to dynamically optimize the fusion weights of the eye and facial features. Furthermore, the improved convolutional block attention module (CBAM) [37] employs parallel spatial and channel attention mechanisms to achieve feature compression, information interaction, and weight adaptive adjustment, thereby enhancing the fusion of eye and facial features. Finally, the high-dimensional features extracted by the eye stream are combined with the features after integration from the fusion stream and processed through two fully connected layers to complete the gaze estimation task.
# Gear Fault Detection Method Based on Convex Hull Clustering of Autoencoder's Latent Space
###### Abstract
Based on the performed calculations, we may state that:  
* The proposed methods based on autoencoders were efficient and could detect even the initial state of pitting formation, which may be difficult with the aid of signal analysis in the time and frequency domain;
* The best model (AE+CH) showed a very high effectiveness (100%) in failure detection (true positive) and 98.9% in normal state prediction (true negative), which resulted in a very high F1-measure (0.99);
* The latent space analysis revealed a generalized perspective on the gear wear--the measurements drifted in a specific direction in the latent space with the progress of gearbox damage;
* The autoencoder outperformed the generative adversarial network in terms of generalization on wear prediction.
# An Overview of Indoor Localization System for Human Activity Recognition (HAR) in Healthcare
## 3 Signal Measurement Techniques
### Receiving Angle  
Angle of Arrival  
This is based on determining the angles between the propagation direction of the received signal and two or more predetermined references [28]. At least two AOA measurements from two different references are necessary to estimate a mobile's position location (Figure 6). To improve accuracy, three beacons or more are used for position estimation. This measurement can be made using directional antennas or antenna arrays. The position estimation is performed by comparing the carrier phase or signal amplitude across multiple antennas. With AOA, no time synchronization between nodes is required. This technique, however, has limitations because, as distance increases, accuracy decreases. It is affected by the phenomenon of multipath and NLOS.  
Figure 5: Time difference of arrival.
# A Review Study on Total Ankle Replacement
## 3 Revision TAR  
Revision TAR is performed for a variety of reasons: implant failure due to septic or aseptic loosening, component subsidence, or mechanical impingement.  
The rate of major revision in TAR is low, at \(<\)5% at 5 years [48]. A systemic review of clinical studies on TAR revision rates over the last decades showed a 12.6% revision rate within 7 years after surgery [49].  
However, revision rates continue to be nearly double those seen in total hip and knee replacements. This is partly due to the different biomechanical factors that affect the ankle joint. The small contact area between the talus and tibia experiences forces four to seven times body weight with normal walking, resulting in extremely high contact stresses in TAR. In conjunction with the relatively low yield point and wear resistance of ultra-high molecular weight polyethylene (UHMWPE), these high stresses contribute greatly to failures in the TAR liners. One cause of aseptic loosening is wear debris created during joint articulation. A study found that up to 54% of TAR revision surgeries may be directly or indirectly caused by the wear of UHMWPE liners. Because UHMWPE wear not only contributes to instability in the joint as the surface decays but also produces UHMWPE debris, these liners are one of the main causes of revision [43].  
Despite increases in TAR prevalence and surgical volume over the last two decades, TAR is still a significantly less commonly performed surgery compared to total knee and total hip replacements. Reoperation rates have been shown to be higher in surgical centers or by surgeons that perform \(<\) 20 TAR a year, demonstrating that there is a learning curve and technical skill plays a role in successful outcomes [50].  
Moreover, as with other joint replacements, appropriate patient selection is critical, as patient factors play a role in TAR failure. Diabetes, peripheral vascular disease, chronic pulmonary disease and alcohol abuse have been shown to significantly increase risk for early failure within 5 years post operatively [51]. Despite traditional recommendations to reserve TAR for older patients, analysis of 10-year implant survivorship showed no significant difference in revision rates between patients older than 50 and those younger than 50 years old [52].  
Dynamic ultrasonography has been successfully used in cadaveric models to evaluate motion at the bone-prosthesis interface in TAR. Dynamic ultrasonography can directly visualize motion at the bone-prosthesis interface, in contrast to plain radiograph and CT scanning, where static images are evaluated for lucencies that suggest loosening. This could possibly allow for early identification of component aseptic loosening, prior to loss of significant bony architecture [53; 54; 55].
# Efficient Data Gathering in 3D Linear Underwater Wireless Sensor Networks Using Sink Mobility
## 4 Framework and Formal Definition of the Problem  
Here, we describe the nodes' relation with the MS or the CNs' relation. We will also explain the network model.