esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Results and Discussion
For instance, at an average SNR of 20 dB, the BER is around \(10^{-2}\) under strong turbulence, while it improves to approximately \(10^{-4}\) under weak turbulence. Therefore, Figure 6 presents the outage probability for conditions ranging from weak to strong turbulence with \(\Lambda=3\) dB. Our series solutions closely match the exact outage curves with \(J=30\) in practical SNR regimes. Figure 4: BER performance of coherent THz links for two different turbulent channels with \(z=200\) m.  
Figure 5: BER of coherent THz links for different turbulence intensities GG channel with pointing errors for a distance \(z=1000\) m.
## 1 Introduction
### Laboratory Experiments on Fangshan Granite Samples  
#### 2.1.1 Granite Samples and Experimental Methods  
The granite samples were collected from a quarry in Fangshan, Beijing, and the cylindrical specimens with diameter of 25 mm and height of 50 mm (Figure 1) were well prepared for the experiments in this study. The samples are unweathered, with the color of gray white, and no obvious cracks can be observed by naked eyes. The average density is about 2.72 g/cm\({}^{3}\). According to XRD analysis, the samples have the mineral composition of quartz (38%), plagioclase (47%), microcline (11%), and mica (4%) [29].  
The main apparatus used in this experimental study includes: (1) SG-XL1200 furnace (developed by SIOM in Shanghai, China) with the maximum heating temperature of 1200 \({}^{\circ}\)C, and the heating rate can be controlled with a real-time temperature control accuracy of \(\pm 3\)\({}^{\circ}\)C; (2) DHG-9053A drying oven (produced by Yiheng Equipment, Shanghai, China) with the temperature control accuracy of \(\pm 2\)\({}^{\circ}\)C; (3) TAW-2000 servo-controlled triaxial testing system for rock specimens (produced by Chaoyang Test Instrument Co., Ltd., Changchun, China).  
In this experiment, the Fangshan granite samples were first heated to the target temperatures (200 \({}^{\circ}\)C, 400 \({}^{\circ}\)C, 600 \({}^{\circ}\)C, 800 \({}^{\circ}\)C) at a constant rate of 3 \({}^{\circ}\)C/min, then the target temperature was maintained for each of the rock samples for 2 h. Thereafter, the rock samples were immediately put into water (initial water temperature was 20 \({}^{\circ}\)C, water volume was about 20 L) and cooled for 2 h in order to make the rock samples fully cooled. Finally, the rock samples were dried at 103 \({}^{\circ}\)C for 48 h, and a series of uniaxial and triaxial compression tests (\(\sigma_{2}=\sigma_{3}=0\), 10, 20, and 30 MPa) were carried out on these samples. For the uniaxial test, \(\sigma_{1}\) increases continuously with \(\sigma_{2}\) and \(\sigma_{3}\) remaining zero until the failure of the sample occurs. For the triaxial test, the cylindrical sample is put into the loading cell firstly, and the confining pressure is applied with oil, until the initial stress state (\(\sigma_{1}=\sigma_{2}=\sigma_{3}\)) is reached, thereafter the confining pressures (\(\sigma_{2}=\sigma_{3}\)) is kept constant, and the axial stress \(\sigma_{1}\) is increased until the failure of the sample occurs.
## 3 Modified PROS with Local Search Mechanism
### Quadratic-Distributed Random Orthogonal Search (QROS)  
In this section, we present our second proposed algorithm called Quadratic-Distributed Random Orthogonal Search (QROS). The QROS algorithm (Algorithm 3) is presented as follows:  
```
input :nil output: the best solution vector \(\mathbf{x}^{(t)}\) found by the algorithm \(t\gets 0\);  Initialize \(\mathbf{x}^{(t)}=(x_{1},x_{2},\ldots,x_{D})\) randomly from \(\Omega=[a_{1},b_{1}]\times[a_{2},b_{2}]\times\cdots\times[a_{D},b_{D}]\); repeat  Sample a random integer \(j\in\{1,\ldots,D\}\);  Sample a random real number \(r\sim Q(a_{j},b_{j},x_{j}^{(t)})\) ; \(\mathbf{y}\leftarrow\mathbf{x}^{(t)}\); \(y_{j}\gets r\); if\(f(\mathbf{y})<f(\mathbf{x}^{(t)})\)then \(\mathbf{x}^{(t+1)}\leftarrow\mathbf{y}\); else \(\mathbf{x}^{(t+1)}\leftarrow\mathbf{x}^{(t)}\);  end if \(t\gets t+1\); until a termination criterion is met; return\(\mathbf{x}^{(t)}\)
```  
**Algorithm 3**Quadratic-Distributed Random Orthogonal Search (QROS)  
Figure 1: The probability density function of the triangular distribution \(\mathcal{T}(a,b,c)\), the quadratic distribution \(Q(a,b,c)\), and uniform distribution \(\mathcal{U}(a,b)\).  
Compared with TROS, QROS has one change in line 5 of Algorithm 2. The value of the \(j\)-th decision variable is sampled using the quadratic distribution \(\mathcal{Q}\). The probability density function of the quadratic distribution \(\mathcal{Q}(a,b,c)\) is  
\[f_{\mathcal{Q}}(x)=\begin{cases}\alpha_{\mathcal{Q}}(x-a)^{2}&,a<x\leq c\\ \beta_{\mathcal{Q}}(x-b)^{2}&,c<x<b\\ 0&,x\leq a\text{ or }x\geq b\end{cases} \tag{3}\]  
where \(\alpha_{\mathcal{Q}}=\frac{3}{(b-a)(c-a)^{2}}\), \(\beta_{\mathcal{Q}}=\frac{3}{(b-a)(c-b)^{2}}\), \(a\), \(b\) and \(c\) are the lower limit of \(x\), upper limit of \(x\) and mode of \(x\), respectively. The quadratic distribution is illustrated in Figure 1.  
In each iteration of QROS, the next point is sampled using the quadratic distribution with the settings \(a=a_{j},b=b_{j},c=x_{j}^{(t)}\) where \(j\in\{1,2,\ldots,D\}\) is the randomly chosen decision variable for the current iteration, \(a_{j}\) and \(b_{j}\) are the lower bound and upper bound of the \(j\)-th decision variable, and \(x_{j}^{(t)}\) is the value of the \(j\)-th decision variable of the current best solution vector. With this distribution, there is a higher chance to draw a sample that is near to the current position \(x_{j}^{(t)}\) than is far from the current position. Compared with TROS, QROS encourages exploitation even more than TROS.  
It has to be noted that both TROS and QROS are still \"parameter-free\" algorithms, as \(a\) and \(b\) are fixed boundaries, and \(c\) is determined based on the current best solution vector. With a simple modification, both TROS and QROS are able to improve the convergence speed of PROS and keep the major properties of PROS: parameterless, excellent computational efficiency, easy to apply to all kinds of applications, and high performance with finite-time search budget.
# NICE: Superpixel Segmentation Using Non-Iterative Clustering with Efficiency
## 1 Introduction
However, SNIC overly relies on the priority queue to achieve non-iterative optimization. In fact, that is a greedy strategy and is prone to get trapped in a premature convergence problem than SLIC [18]. In addition, even if SNIC can be performed with high efficiency, its theoretical complexity is not always lower than SLIC [19]. On the other hand, SNIC adopts a rigid region growing method to generate superpixels, in which clustering centroids are evolved using online averaging. Similar to some SLIC-like methods, SNIC may suffer from the color-spatial compactness that goes against the local homogeneity, especially in complicated texture and low contrast regions. To fulfill the aforementioned requirements, this paper proposes a new superpixel segmentation algorithm, referred to as non-iterative clustering with efficiency (NICE). It improves the conventional SNIC in two aspects. Firstly, a new strategy termed Elimination of Inspection Redundancy (EIR) is proposed. It introduces inter-pixel correlation as a local smooth cue to determine the priority for pixels with similar appearance. Therefore, large amounts of repetitions during the inspection process are avoided. By calculating neighboring information in this manner, weak boundaries between low contrast regions could be well preserved. In addition, an Accelerated Implementation based on Recursion (AIR) partially re-organizes the original SNIC. A last-in-first-out (LIFO) stack is introduced to speed up sorting local pixels in the priority queue. During the joint online assignment and updating step, a large number of pixels are processed in a batch, which gets rid of frequent push and pop operations, thus it avoids repeated pixel inspecting and sorting. Eventually, NICE combines EIR and AIR into SNIC framework in a subtle way, which inherits the desirable properties of original SNIC and the property of the two strategies. To the best knowledge of the authors, this work is the first trial to optimize the SNIC algorithm. As its name indicates, the proposed NICE generates comparable superpixels with respect to boundary recall, under-segmentation error and achievable segmentation accuracy. It is more efficient and achievesapproximately 24 fps for a 481 \(\times\) 321 image that exceeds SNIC's 16 fps without any parallelization or GPU processing. This paper is organized as follows. The conventional SNIC framework is reviewed in the next section. In Section 3, the proposed two strategies EIR and AIR, as well as the NICE framework are presented in detail, respectively. Qualitative and quantitative analyses are explicated in Section 4. Finally, Section 5 makes a brief conclusion.
# Improving a Street-Based Geocoding Algorithm Using Machine Learning Techniques
## 3 Geocoding Algorithm Using Machine Learning Techniques
### Address Locating
```
1:Input:\(num\), addressDB, perp_offset, mid_offset, end_offset,
2:Output:\(x\), \(y\)
3:Algorithm
4:Initialize the parameters of the _dirc_, \(\theta_{0}\), _interpolate_ and _Az_
5:if\(num\) is ODD
6:from addressDB get left side MinRange, MaxRange
7:\(direct=1\)
8:else
9:from addressDB get right side MinRange, MaxRange
10:\(direct=0\)
11:from addressDB get \(X_{to}\), \(X_{from}\), \(Y_{to}\), \(Y_{from}\)
12:\(\theta_{0}\) = calculateSouth Azimuth (\(X_{to}\), \(X_{from}\), \(Y_{to}\), \(Y_{from}\))
13:\(X_{start}=X_{from}-end\_offset\)\({}^{*}\)sin(\(\theta_{0}\))
14:\(Y_{start}=Y_{from}-end\_offset\)\({}^{*}\)cos(\(\theta_{0}\))
15:interpolate = (\(num\)-MinRange)(MaxRange-MinRange)
16:\(Az=\theta_{0}-90\)
17:\(x=X_{start}+(p)^{*}\)(\(X_{end}\) - \(X_{start}\)) - mid_offset\({}^{*}\)sin(\(Az\)) - \(direct\)\({}^{*}\)perp_offset\({}^{*}\)sin(\(Az\))
18:\(y=Y_{start}+(p)^{*}\)(\(Y_{end}\) - \(Y_{start}\)) - mid_offset\({}^{*}\)cos(\(Az\)) - \(direct\)\({}^{*}\)perp_offset\({}^{*}\)cos(\(Az\))
```  
**Algorithm 4** Choosing optimal thresholds for similarity metrics  
Figure 6: Locating the coordinates of the geocoded address.
## 3 Results and Discussion
### Mineral Composition  
The mineral composition of mushroom powders and base flour is summarized in Table 2. In turn, Table 3 presents the mineral content of cookies enriched with mushroom flour. Compared with wheat flour, mushroom lyophilates are characterized by significantly (\(p<0.05\)) higher amounts of most of the analyzed elements: Mg, K, Na, Fe, Zn, Cu, Mn, and Ni. The content of toxic elements (Cd, Pb) in the mushroom powders is within the standards set by European Union regulations [45]. Similarly, Pb and Cd in the cookies were not recorded, nor was sodium (Na). There were no significant (\(p<0.05\)) differences in the content of elements such as Ca, Mn, Se, Co, or Ni between the variants of the obtained cookies. Higher levels of Zn were recorded in all cookies with the addition of mushroom powders (from 7.97 to 10.73 mg/kg dw) compared with the control cookies (6.99 mg/kg dw). The use of lyophilistate from _A. bisporus_ in amounts of 2-6% contributed to a significant increase in the Cu content in baked goods, in the range from 1.83 to 2.83 mg/kg dw, compared with the control (1.36 mg/kg dw). Inclusion of at least 2% dried oyster mushroom contributed to a more than twofold higher K content in the supplemented cookies (203.14 mg/kg dw) compared with the content of this element in the control samples (81.52 mg/kg dw). In cookies where part of the base flour was substituted with button and oyster mushroom powder from 4% to 6%, higher amounts of iron or magnesium were recorded. The increase in most minerals in mushroom-enriched cookies was the result of the higher amounts of ash in the mushroom powders (Table 1). As shown by the results presented in other works, the addition of mushroom powder leads to an increase in the ash and element content in composite flours where the base is sweet potato and rice flour [73] or maize flour [74]. [PERSON] et al. [6] observed an increase in the amounts of ash and elements (Zn, Fe, K) in cookies where part of the wheat flour was replaced with _Calocyle indica_ flour. [PERSON] et al. [23] recorded a significant (\(p<0.05\)) increase in the amounts of ash and elements such as Na, Ca, K, and Zn in cookies prepared from wheat flour with the addition of oyster mushroom flour. The authors suggested that the biscuits they obtained would be suitable for people on a low salt diet, because the biscuits had a low sodium to potassium ratio (below 1). The amounts of individual elements in mushrooms are the result of many factors, including species, part of the fruiting body (e.g., cap, stipe), fungal lifestyle (e.g., saprotrophs), and type of growing medium [43; 75]. According to the literature data [11; 13; 76], the amounts of major essential elements in cultivated mushrooms varied; in the case of Ca it was 860-1400 mg/kg dw and 190-1500 mg/kg dw; for K it was from 18,321-49,000 mg/kg dw and 21,840-51,000 mg/kg dw; for Mg it was from 1099-1480 mg/kg dw and
## 5 Discussion
### Uncertainties and the Significances of the TVP Model  
Some studies have used ground-based data to validate the CO\({}_{2}\) concentration estimated by TANSO and SCIAMACHY and found different results in different regions. TANSO XCO\({}_{2}\) with six ground-based Total Carbon Column Observing Network [78] sites around the world have been compared and found that the average bias of TANSO XCO\({}_{2}\) was 2.80 ppm [28]. Researchers also found that the computed errors of TANSO XCO\({}_{2}\) varied between 1.20 and 3.20 ppm [72]. With regard to the accuracy of SCIAMACHY CO\({}_{2}\) concentration, some researchers found that the global single-measurement precision amounted to 2.50 ppm [29,79]. In the present study, we used TANSO XCO\({}_{2}\) as the observed value to check the accuracy of TVP model and found that the accuracy in the study area was between \(-\)2.56\(\sim\)3.14 ppm. Australia exhibited the lowest difference, at \(-\)1.24\(\sim\)1.19 ppm, and the largest difference was in South America, at \(-\)2.09\(\sim\)3.14 ppm. It indicates that the accuracy of TVP model is similar with the SCIAMACHY and TANSO.  
Despite the encouraging performance of the TVP model in estimating CO\({}_{2}\) concentration from MODIS products, these estimates still contain significant uncertainties and are a long way from reliably estimating CO\({}_{2}\) concentration from remote sensing data. Although random error is usually considered less serious than uncertainties [31], there can be many sources of error that create a high overall uncertainty. The CO\({}_{2}\) exchange between terrestrial ecosystems and the atmosphere has been mainly dominated by three factors: climate, land-cover type and human activities [4,80,81,82]. With regard to the TVP model in the present study, there are several sources of uncertainty associated with the CO\({}_{2}\) concentration estimates: uncertainly in the TANSO accuracy, uncertainty in land cover, uncertainty in the input data and uncertainty regarding the extent and nature of climate change in different regions. Thus, there remains a great deal of research is still required to ensure the accuracy of CO\({}_{2}\) concentration estimated based on remote sensing data.  
Although the TVP model itself has some shortcomings but it is a meaningful explore in CO\({}_{2}\) calculate using remote sensing data. The same as used MODIS, we can also use NOAA AVHRR data combine with ground stations data to derive CO\({}_{2}\) and CH\({}_{4}\) concentrations at regional or global scale. Using the TVP model we can derive global CO\({}_{2}\) concentration from 1981 to 2002 when there was no GHG satellite. In fact, we can also use TVP model to identify the spatial distribution of GHG concentrations which is also an important issue nowadays. Moreover, in the future, with the launching  
Figure 4: Cont.  
of other GHG observe satellites, e.g., Orbiting Carbon Observatory 2 (OCO-2), Carbon Monitoring Satellite (CarbonSat) and GOSAT-2, the accuracy of TVP model will increasing.
specifically thought for wireless sensor networks [3]. Similarly to MQTT, MQTT-SN implements quality of service control at the application layer, defining different QoS levels ranging from a simple \"fire-and-forget\" (not acknowledged) delivery to a more complex four-way handshake mode ensuring an \"exactly once\" delivery and avoiding message duplicates. QoS control is implemented in a user-centric fashion; indeed, each MQTT-SN client may select a specific QoS level for its publications or subscriptions, which in turn determine how messages are exchanged by the broker. Since MQTT-SN runs over UDP and lacks the error control provided by TCP, QoS at the application layer is fundamental in those situations when the underlying network is inherently lossy, as is the case of wireless networks, as it may constitute the only mechanism to ensure correct message delivery.  
However, leaving the QoS control to each client has some drawbacks: first, the relationship between a specific MQTT-SN QoS level and the corresponding delivery performance (e.g., in terms of latency or packet loss) is not generally known to a user, as it strongly depends on the underlying network characteristics. A client may therefore request a QoS level that is too low (in case the network conditions are adverse) or too high (in the opposite case) for the desired delivery performance. At the same time, providing clients with sophisticated tools to select the optimal QoS level, such as, e.g., network bandwidth estimation [4], may be prohibitive due to the limited available resources.  
To overcome such issues, this paper proposes an adaptive QoS controller that is able to tune the optimal QoS level for each node, based on specific delivery performance constraints. The contributions of this paper are the following: first, we design the QoS controller in a centralized fashion, running on top of the MQTT-SN broker; the controller gathers network statistics periodically and is able to tune the QoS of each node in order to match user-defined performance constraints in terms of end-to-end latency and packet loss rate. Second, we implement the controller in a simulated environment based on the NS-3 framework: for the task at hand, we embed into the simulator the MQTT-SN protocol and run it on top of a 802.11b IoT network composed of several clients as well as interfering nodes. Finally, we perform extensive experiments and evaluate the effectiveness of the QoS controller compared to a traditional scenario where the QoS level choice is left to the user.  
The remainder of this paper is structured as it follows: Section 2 contains a review of the related literature, while Section 3 gives a background on the MQTT-SN protocol with emphasis on the QoS levels, and describes the simulation environment implemented using the NS-3 framework. The features of the QoS controller are described in Section 4, while Section 5 reports the results of the performed experimental evaluation. Finally, Section 6 concludes the work with some final remarks and future research directions.
###### Abstract  
The emergence of the vibration energy harvesting system makes it possible for wireless monitoring nodes in coal mines to realize self-power supply. In order to reveal the influence of gravity effect on the response characteristics of the combined beam tri-stable piezoelectric energy harvesting system (CTEHS), the system's nonlinear magnetism is calculated according to the principle of point magnetic charge dipole, and the system's nonlinear resilience is obtained through experimental measurements and nonlinear fitting methods. Based on the Lagrange equation, the system's electromechanical coupling motion model considering gravity is established. The system's motion equation is solved numerically based on the Runge-Kutta algorithm, and the effects of the end magnet mass and the initial vibration point on the bifurcation behavior, potential energy, and system output performance are investigated by emulation and experiment. The research shows that the magnet's gravity effect causes a change in the stable equilibrium position and the system's motion state and also causes the system to generate additional gravitational potential energy, which leads to a potential asymmetric well of the system. Under the consideration of magnet gravity, the appropriate end magnet mass and initial vibration point can not only reduce the system's requirements for external excitation strength but also effectively improve the system's response and output. This research provides a new theoretical basis for the optimal design of the tri-stable piezoelectric energy harvesting system.  
tri-stable; magnetic coupling; gravity effect; asymmetric potential well; response characteristics +
Footnote †: journal:  
Article Modeling and Characteristic Analysis of Combined Tri-Stable Piezoelectric Energy Harvesting System Considering Gravity
## 3 Results and Discussion
Figure 3.6 Frequency dependence of \(C_{0}\) (**a**) and values of attenuation constant of \(\alpha\) (**b**) of sample B in the range of 2–18 GHz. In Table 1, the recently reported EMW absorption performances of typical Fe\({}_{3}\)O\({}_{4}\) material-based composites, as well as the Fe\({}_{3}\)O\({}_{4}\)/ZnO composites prepared in this work, have been plotted. In comparison with the reported composites in Table 1, it can be observed that the Fe\({}_{3}\)O\({}_{4}\)/ZnO composites have a wide effective absorption bandwidth and a promising negative RL value among these composites. It can be concluded that the as-fabricated Fe\({}_{3}\)O\({}_{4}\)/ZnO nanocomposites with enhanced EMW absorption properties confirm the presence of an efficient complementarity between magnetic and dielectric loss. The above-mentioned advantages indicate that this special core-shell structured absorber is able to meet the requirements of ideal MAMs.
## 3 The Proposed Scheme
### Registration
In Section 5, we also analyze the proposed ARP table's solution and find that it requires much less data storage compared to other solutions in related works. The communication needs to include a key exchange process, where we recommend the usage of the elliptic curve Diffie-Hellman key exchange. While ARP tables are essential for simplifying device communication, they also pose security risks. Network participants should never share their ARP table with anyone, as it can expose symmetric keys, compromising the device's communication channels. A malicious attacker with access to a complete ARP table can impersonate a compromised device, participate in the network, and access sensitive data. The best solution is to avoid sharing any information present in the ARP tables over the network, except for device addresses. A possible solution to preserve the confidentiality of the values in the ARP table is to encrypt the table data with the PUF value. Even if a device falls into the hands of hostile entities or an unsecured area, the encrypted ARP table minimizes the risk of device hacking.