esa-sceva/satcom-chunk-collection huggingface dataset:
# Veins Depth Estimation Using Diffused Reflectance Parameter
## 3 Proposed Method  
With conventional imaging techniques, the depth of blood vessels cannot be estimated accurately. In this paper a technique for the estimation of veins depth by means of diffused reflectance parameter is presented. When the light beam strikes on a scattering medium (e.g., skin), a subsequent part of the beam energy (i.e., photons) is either reflected, absorbed or transmitted through the medium. The reflected part contains the components of specular reflection and diffused reflection. Specular reflection is the reflection from the surface of medium while diffused reflection occurs due to the subsurface scattering of the layered medium [17].  
In the proposed method, the diffused reflected light from the skin is used to approximate the depth of veins. When light propagates in the biological scattering structures, the photons scatter by striking against the cellular structure. This striking mechanism force them to deviate from their path and follow a new one. This scattered light ultimately leaves the medium. The physical location of absorbing elements like veins defines the intensity of backscattered beam. In the four-layered skin model, veins are supposed to be present in the hypodermis (4 th) layer. The light distribution is considered uniform on the whole surface in order to have known intensity of incident light (I\({}_{0}\)) per unit area. The mathematical model is used to calculate the estimated depth of veins in the 4 th layer of skin model based on the diffused reflected intensity measured by the camera sensor. The four-layered skin model is depicted in Figure 1 with the illumination and reflectance image acquisition setup. The NIR light is focused on the surface of model through a diffuser in order to make the illumination uniform. The reflectance image is captured with the camera sensor.  
In the layered anatomy of skin, the primary, thin and callous layer is called stratum corneum. This layer is part of epidermis layer but here it is modeled separately due to its highly scattering behavior. Stratum corneum reflects about 5 to 7% of the incident light [18,19] depending upon the incident angles. The millions of dead cells in this layer are being substituted by the living cells in a continuous course. The second major skin layer is called epidermis. This layer contains the melanin pigmentations which are in charge of skin color and have high absorption coefficient. In order to protect the inner body, these melanin pigmentations absorb harmful radiation incident on the skin from the external sources (e.g., Sun). The third layer is called dermis which comprises of sweat glands, hair follicles, collagen and fibrous tissues. The fourth layer represents the hypodermis layer of skin which contains subcutaneous tissues. This layer encompasses the blood vessels along with the connective tissues and fat. These blood vessels are used for blood extraction and IV procedures. The block diagram of the proposed method is given in Figure 2.  
Figure 1: Setup to measure diffused reflectance in four-layers skin model with varying vessel’s depth.
## 2 Materials and Methods
### Phase-Based Step Frequency Estimator
#### 2.1.2 Real-Time Step Frequency Computation  
To make the phase variable of two adjacent gait cycles continuous (calculation of slope requires continuous curve), the continuous gait phase variable \(\varphi_{con}\) is computed through Equation (11).  
\[\varphi_{con}(t)=2\pi*G_{t}(t)+\overline{\varphi}_{ln}(t) \tag{11}\]  
where \(G_{t}\) is the number of gait cycles that have been passed up to time \(t\). The continuous gait phase variable \(\varphi_{con}\) is a continuous and monotonically increasing gait phase variable during walking as shown in Figure 2b. The purple point represents the current time \(t\) and the green point represents the time \(t^{\prime}\). The interval \(t_{i}=t-t^{\prime}=1\) s is given in advance. Two points are connected by a solid line segment and the dotted line is horizontal. The angle between the two lines is marked as \(\theta\).  
Figure 2: (**a**) Phase-time diagram before filtering and after filtering. (**b**) The y-axis shows the continuous phase and the x-axis is time.  
The continuous gait phase variable is increased by \(2\pi\) radians after one gait cycle \(T_{G}(t)\). So, the slope of the continuous gait phase variable in real-time is  
\[\tan[\theta(t)]=\frac{2\pi}{T_{G}(t)} \tag{12}\]  
The real-time step frequency can be estimated by  
\[f_{step}(t)=\frac{1}{T_{G}(t)}=\frac{\tan[\theta(t)]}{2\pi}=\frac{\varphi_{con} (t)-\varphi_{con}(t-t_{i})}{2\pi(t-t_{i})} \tag{13}\]
## 2 Zone-Based Registration Scheme
### Type I: Serial Shape
#### 2.1.2 Case 2: Processing Implicit Registration inside Serial-Shaped RA  
Figure 6 shows the state transition probability and paging procedure for two states (\(I_{21}^{I}\) and \(I_{12}^{I}\)). We also assumed that the UE moved from 3\(\rightarrow\)2\(\rightarrow\)1 and that Network_DB stored \(\left\lceil\frac{1}{2}|2|3\right\rceil\). State \(I_{21}^{I}\) represents the state in which the implicit registration is processed after the UE moves from zone 2 to the edge area (zone 1 or 3). Similarly, \(I_{12}^{I}\) defines the state in which implicit registration is performed after the UE moves from the boundary to the middle zone (zone 2). The transition process from state \(I_{12}^{I}\) typically includes the following states:  
Figure 5: State transition probability for Case 1 of Type I.  
(_i_) \(I_{12}^{I}\): If implicit registration occurs after UE moves from zone 1 to 2 in the middle, the Network_DB will be changed to \(\boxed{2|1|3}\). With the arrival of an incoming call before moving to another zone, UE can be found in zone 2 through one paging procedure. The transiting probability to \(I_{12}^{I}\) is \(1-m^{\prime}\).  
(_ii_) \(C_{21}^{I}\): UE can enter state \(C_{21}^{I}\) with a probability of \(\theta m^{\prime}\) if it returns to zone 1 after an implicit registration in zone 2. Two paging operations (2\(\rightarrow\)1) are required for an incoming call after arriving at this state.  
(_iii_) \(E_{21}^{II}\): The probability that the UE will make a new RA of Type II, that is, the transition probability from state \(I_{12}^{I}\) to \(E_{21}^{II}\), is \(2(1-\theta)m^{\prime}/5\). To connect an incoming call in the newly registered zone, one paging procedure is necessary.  
(_iv_) \(E_{21}^{I}\): UE can move to state \(E_{21}^{I}\) with a probability of \(2(1-\theta)m^{\prime}/5\) if it constructs the new RA of Type I. An incoming call connection in the new zone is possible at once.  
(_v_) \(C_{23}^{I}\): When the UE moves to zone 3 from zone 2, it moves to state \(C_{23}^{I}\) with a probability of \((1-\theta)m^{\prime}/5\). Since Network_DB is \(\boxed{2|1|3}\), three paging procedures (2\(\rightarrow\)1\(\rightarrow\)3) in total are performed to locate UE in zone 3.
###### Abstract
[13] generated high-resolution fog fields by blending GK2A fog detection results with visibility-based objective gridded data, thereby improving detection beneath cloud cover and in regions with limited surface observations. GK2A fog detection also provides a foundation for integrating gridded visibility data and CCTV (closed circuit television)-derived visibility data to support road visibility estimation [14]. An XGBoost-based model was developed to achieve continuous detection of FLS by utilizing both reflectance and brightness temperature channels derived from MSG/SEVIRI (Spinning Enhanced Visible Infrared Imager) [29]. With increasing demand from meteorological forecasting, aviation, maritime, and surface transportation safety, continual improvements to the GK2A_FDA are necessary. Although GK2A fog detection is used operationally, most evaluations have focused on fog events [21; 28; 30; 31]. A comprehensive performance evaluation using year-round data has become imperative to ensure the robustness of the GK2A_FDA. Considering surface background types, regional variations in fog characteristics across South Korea, and time-dependent differences in satellite channel usage, the GK2A_FDA is divided into nine sub-algorithms based on region (inland, coast, and ocean) and time of day (daytime, twilight, and nighttime) [20; 28; 31]. This study presents detailed statistical analysis and case studies of six sub-algorithms--daytime, nighttime, and twilight algorithms for both inland and coastal regions--that can be evaluated using ground-based visibility observations. The ocean algorithms are excluded from this analysis due to the lack of ground-based validation data over ocean areas. The results provide a foundation for future improvements. Section 2 describes the data and methodology; Section 3 presents performance evaluation and case studies; Section 4 provides a general discussion including improvement strategies; and Section 5 summarizes the conclusions.
# Analysis of Erosion Minimization for a Slurry Pump Using Discrete Phase Model Simulations
## 3 CFD Analysis
### Analysis of the Simulation Results
#### 3.2.2 Analysis of the Erosion Rate Density (E) Based on the Impeller Speed (v)  
Figure 8 presents the erosion rate density based on the impeller rotation speed. The particle diameter and gap between the impeller and the throat bush were fixed at 44 \(\upmu\)m and 1 mm, respectively. However, the impeller rotation speed was varied from 550 to 600 rpm and then to 650 rpm. As the impeller rotation speed increased, the erosion rate density increased. It increased more when the impeller rotation speed increased from 550 to 600 rpm compared with when the speed increased from 600 to 650 rpm. In general, if the rotational speed of the impeller increases, the speed of the internal flow also increases, and the impact wear also increases. Similar to the previous result, the highest erosion rate density was observed near \(\alpha=45^{\circ}\) and \(\alpha=315^{\circ}\), which were close to the outlet.  
Figure 8: Erosion rate density based on the distance between the parts.  
Figure 7: Erosion rate density based on the distance between the parts.  
3.2.3. Analysis of the Erosion Rate Density (E) Based on the Slurry Particle Diameter (d)  
Figure 9 shows the erosion rate density based on the slurry particle diameter in microns. Here, the impeller rotation speed and the gap between the impeller and the throat bush were fixed at 600 rpm and 1 mm, respectively. However, the particle diameter was varied from 37 to 44 \(\upmu\)m and then to 53 \(\upmu\)m. As the particle diameter increased, the erosion rate density increased. However, the erosion rate density in the vicinity of \(\alpha=45^{\circ}\) was higher when the particle diameter was 44 \(\upmu\)m than when the diameter was 53 \(\upmu\)m. This trend is because larger particles cause less impact wear but are more susceptible to sliding wear [16]. Similar to the previous results, the highest erosion rate density occurred near \(\alpha=45^{\circ}\) and \(\alpha=315^{\circ}\), which were close to the outlet.
## 3 Results  
After the enrichment process with pig slurry, NEZ gains a total N load of 5.84 (\(\pm\) 0.90) g kg\({}^{-1}\) (Table 1). Most of the N was present in the form of Exch N-NH4\({}^{+}\), while Fix N-NH4\({}^{+}\) is considerably lower. N-NO3\({}^{-}\) load is on average 148 mg kg\({}^{-1}\) (the material is not washed after the treatment before the introduction into the soil system). NEZ is characterized by a very high N\({}_{\rm mic}\) and a low C\({}_{\rm mic}\), resulting in a C\({}_{\rm mic}\)/N\({}_{\rm mic}\) ratio lower than 1. TOC content is quite low (10.3 g kg\({}^{-1}\) on average) resulting in a low C/N. Soil pH was not affected by the addition of both NZ and NEZ (\(p>0.05\)), remaining close to neutral values in all treatments (with exception of Jun-2013 where after urea addition an increase toward sub-alkaline values (\(p<0.05\)), Figure 3). CEC increased significantly (\(p<0.05\)) in the bulk soil of all ZT. Ca\({}^{2+}\) and Mg\({}^{2+}\) were the major exchangeable bases, followed by K\({}^{+}\) and Na\({}^{+}\), with the latter always higher in 7 CZ than in the other treatments (Table 2). No significant differences were found between the various treatments regarding exchangeable K\({}^{+}\) (\(p>0.05\)), while Ca\({}^{2+}\) and Mg\({}^{2+}\) are generally higher in ZT, especially in the bulk soil with respect to the rhizosphere (\(p<0.05\)).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**TN** & g\(\cdot\)kg\({}^{-1}\) & 5.84 (0.90) \\ \hline
**Fix N-NH4\({}^{+}\)** & mg\(\cdot\)kg\({}^{-1}\) & 343 (21) \\ \hline
**Exch N-NH4\({}^{+}\)** & mg\(\cdot\)kg\({}^{-1}\) & 4884 (330) \\ \hline
**N-NO3\({}^{-}\)** & mg\(\cdot\)kg\({}^{-1}\) & 148 (7.4) \\ \hline
**N\({}_{\rm mic}\)** & mg\(\cdot\)kg\({}^{-1}\) & 876 (152) \\ \hline
**TOC** & g\(\cdot\)kg\({}^{-1}\) & 10.3 (0.1) \\ \hline
**C\({}_{\rm mic}\)** & mg\(\cdot\)kg\({}^{-1}\) & 117 (1) \\ \hline
**C/N** & & 1.78 \\ \hline
**C\({}_{\rm mic}\)/N\({}_{\rm mic}\)** & & 0.13 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average N and C pools of NH4\({}^{+}\)-enriched zeolitite (NEZ). Standard deviation within brackets.  
Figure 3: Mean soil pH for each treatment. Error bars represent standard deviation.
# The Effect of Blue Noise on the Optimization Ability of Hopfield Neural Network
###### Abstract  
Noise is ubiquitous in the real-world environment. At present, most scholars only include the stage of Gaussian white noise when applying noise in neural networks and regard white noise as a tool to optimize the network model, which is far from enough, because noise not only affects the optimization ability of the Hopfield neural network but can also better fit the needs of the actual use of the scene. Therefore, according to the problems in the existing research, a method is proposed to combine the neural network with colored noise according to the signal-to-noise ratio. Taking blue noise as an example, the anti-interference ability of the Hopfield neural network regarding colored noise is studied. The results show that for the Hopfield neural network driven by blue noise, by adjusting the neural network step size, excitation function and signal-to-noise ratio, it not only provides ideas for adding colored noise to the neural network but also enables the neural network model to have better optimization-seeking ability. The research results have some reference significance for improving the practical application of neural networks in noisy environments.  
neural network; optimization problem; colored noise generator; blue noise +
Footnote †: journal: Applied
## 3 Results
### Model Comparison  
A requirement of this work is to compare the practical application of each selected model to verify the effectiveness of the proposed PCIs in crash severity evaluation. The comparison results for different severity levels after the 10-fold cross-validation and parameter optimization are shown in Table 3. In each ML method, the AF combination outperforms other combinations in severity classification, with an average of accuracy of 78.05% and an average AUC of 91.59%. By contrast, the average accuracy using initial crash information (BF feature set) is only 63.70%. This result means that the average accuracy of the six models could increase by 14.35% after the PCIs are applied, with the LCE-PCI model improving by 25.32%. The accuracy of the AF model could only be improved by 1.65% after the continued addition of nine BF variables compared with PCI features. This result demonstrates that the enhancement of the PCIs in classification and the proposed PCI-ML method is sufficient to obtain a good prediction accuracy with a concise form. The classification performance widely varied among the different models. The LCE-AF model can be considered the best evaluation method with the best accuracy of 89.21%, best AUC of 97.23%, and lowest TPR of only 6.45%. The prediction results also show that (1) the MLP models cannot effectively solve the complex nonlinear classification, and the accuracy is still lower than 72% after the addition of PCI features. (2) The RF and SVM methods perform better than the traditional methods and outperform the LCE model for the BF features. (3) The deep learning method (DNN) has been the most advanced technique due to its complex structure in the field of traffic prediction [44], but the misspecification issue
# An Energy Efficient Modulation Scheme for Body-Centric Terahertz (THz) Nanonetworks +
## 3 Trade-Off between Energy Consumption and Data Rate
### Data Rate  
The data rate of the proposed modulation is derived in the following lemma.  
**Lemma 1**.: _The mean data rate of the proposed modulation scheme is_  
\[u(k,t)=\frac{2k}{t(2^{k}+1)}. \tag{2}\]  
Figure 3: Average Energy Consumption.  
Proof.: The mean time required to transmit a bit by using the proposed modulation scheme is calculated by summing the time needed to transmit each symbol and then dividing it by the number of cases multiplied by the number of bits in a symbol, which in short is given by  
\[f(k,t)=\frac{t\sum_{i=1}^{2^{k}}i}{k2^{k}}=\frac{t(2^{k-1}(2^{k}+1))}{k2^{k}}, \tag{3}\]  
which can be written as  
\[f(k,t)=\frac{t(2^{k}+1)}{2k}. \tag{4}\]  
In the above equations \(t\) is the time required for each time slot. Finally, the data rate can be written as  
\[u(k,t)=\frac{1}{f(k,t)}=\frac{2k}{t(2^{k}+1)}\]  
and this completes the proof.  
Figures 4 and 5 compare the proposed modulation with TS-OOK. For illustrative purposes, we use five picoseconds for each time slot, so \(t=5\) ps [7]. Hence, it holds that  
\[f(k,5\times 10^{-12})=\frac{5\times 10^{-12}(2^{k}+1)}{2k} \tag{5}\]  
and  
\[u(k,5\times 10^{-12})=\frac{2k}{5\times 10^{-12}(2^{k}+1)}. \tag{6}\]  
From Figures 4 and 5 we can observe that, in the case of the proposed modulation scheme, the mean time for the transmission of each bit increases with the order of modulation compared to TS-OOK, where the time required for the transmission of each bit is constant. However, as it can be observed in Figure 3, a considerably lower amount of energy is needed to transmit these bits.  
Figure 4: Average Time per symbol.
## 1 Introduction
Another problem is traditional strategies may lead to frequent cache replacement, since they do not restrict the content placement process. This will make the caching router spend more computing resources for caching operations, affecting the quality of packet forwarding and content service, as well as increasing energy consumption. In this paper, our motivation is to design an efficient caching strategy to face the above problems in the ICN scenario equipped with an NRS. Considering that the NRS can retrieve the NAs of content replicas, any caching node has the potential to serve contents to all users. Based on this, our goal is to design a caching strategy to promote the cache hit ratio of caching nodes, as well as a lower latency in content acquisition. For the ICN caching scenario equipped with an NRS, we design a lightweight caching strategy taking into account the information from the NRS. The main contributions of this paper are as follows:  
* We analyze the ICN caching scenario with an NRS and build a network model using the cache hit ratio as the optimization target. Based on the model, different caching strategies are compared and evaluated. * We propose a popularity-based caching strategy with two new features in an ICN caching scenario. One feature is placing the content copy at the on-path caching router with the lowest cache popularity of contents, to gradually push popular contents to the cache close to users. Another is to use number-of-copies control, which is an implicit way to allocate cached resources. These two features allow for a better content distribution in the cache and reduce frequent cache replacement. To the best of our knowledge, our strategy is the first to control the number of content copies. This strategy can reduce the redundancy of content copies and promote the network performance. * We compare the proposed caching strategy with other classic caching strategies in aspects including different topologies and workloads. Our caching strategy showsobvious advantages in terms of the cache hit ratio and server load, meanwhile keeping the user delay of getting contents at a low level. The rest of this paper is organized as follows: at first, we conclude the related work in Section 2. In addition, Section 3 depicts the ICN caching scenario equipped with an NRS as well as the problem formulation. Later, Section 4 describes our proposed caching strategy and the experimental design and results are displayed in Section 5. Section 6 concludes the paper and analyzes some possible future studies.
# Pseudo Random Number Generation through Reinforcement Learning and Recurrent Neural Networks
## 2 Materials and Methods
### Reward and NIST Test Suite  
The NIST statistical test suite for random and pseudo-random number generators is the most popular application used to test the randomness of sequences of bits. It has been published as a result of a comprehensive theoretical and experimental analysis, and may be considered as the state-of-the-art in randomness testing for cryptographic and not cryptographic applications. The test suite has become a standard stage in assessing the outcome of PRNGs shortly after its publication.  
The NIST test suite is based on statistical hypothesis testing and contains a set of statistical tests specially designed to assess different pseudo-random number sequence properties. Each test computes a test statistic value, a function of the input sequence. This value is then used to calculate a \(p\)-value that summarizes the strength of the evidence for the sequence to be random. For more details, see [1].  
If \(S_{t}\) is the sequence produced by the agent at time \(t\), the NIST test suite can be used to compute the average \(p\)-value of all eligible tests run on \(S_{t}\). Some tests return multiple statistic values: in that case, their average is taken. If a test has failed its value for the average, it is set to zero. Some tests are not eligible on certain sequences which are too short, and in this case they are not considered for the average. This average \(\mathrm{avg}_{\mathrm{NIST}}(S_{t})\) is used at the end \(T\) of each episode as a reward function for the MDP:  
\[R_{t}=\begin{cases}\mathrm{avg}_{\mathrm{NIST}}(S_{t})&\text{if $t=T$}\\ 0&\text{otherwise}\end{cases}\]  
This is the same reward strategy used in [6]. Note that, since \(p\)-values are probabilities, rewards belong to \([0,1]\), and that NIST test suite accuracy grows with the tested sequence length.