esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Validation and Discussion
### 3D Tunnel Stability Analysis: \(0\leq L/D\leq 0.5\)  
In the case of \(L/D<0.5\), Equation (18) is still adopted here but the shape factor \(k\) needs to be re-defined. It is assumed \(k\) decreases linearly with \(L/D\) and \(k=k^{0}\) at \(L/D=0\) (i.e., tunnel face). Therefore, the shape factor \(k\) for \(L/D\in[0,0.5]\) can be written as:  
\[k=\Big{(}4-2k^{0}\Big{)}(L/D)+k^{0},\ 0\leq L/D<0.5 \tag{20}\]  
The factor \(k^{0}\) should be determined by the least-squares method, with the database listed in Table 2.  
It should be noted that many studies have focused on tunnel face stability by numerical simulations. However, most of them only gave the upper bound results or conducted calculations in plane strain conditions, which rendered the \"real\" stability number unavailable and these results are excluded.  
To determine \(k^{0}\), an error function \(f\) is defined as:  
\[f=\sum_{i=1}^{m}\big{(}N_{\text{pre}}-N_{0}\big{)}^{2} \tag{21}\]  
where \(N_{0}\) is the stability number in the database (Table 2) and \(N_{\text{pre}}\) is the predicted \(N\) by Equations (18) and (20); \(m\) is the total number of the data in Table 2. The derivation of the error function is:  
\[\frac{\partial f}{\partial k^{0}}=2\sum_{i=1}^{m}\big{(}N_{\text{pre}}-N_{T0} \big{)}\bigg{[}2\ln\bigg{(}2\frac{H}{D}+1\bigg{)}+\frac{1}{\big{(}k^{0}+1 \big{)}^{2}}\bigg{]} \tag{22}\]  
and \(k^{0}\) should be the root of Equation (22) and can be calculated as \(k^{0}=2.36\) after substituting the data in Table 2 into Equation (22). Then the shape factor which is the function of \(L/D\) can be summarized as:  
\[k=\left\{\begin{array}{cc}2-\frac{L/D-0.5}{L/D+0.5},&L/D\geq 0.5\\ 2.36-0.72L/D,&0\leq\ L/D<0.5\end{array}\right. \tag{23}\]  
When \(0\leq L/D<0.5\), the predicted versus measured (or calculated) stability number \(N\) is shown in Figure 8. It is found that the theoretical results agree well with the database in Table 2, which means \(k^{0}=2.36\) is reasonable in the stability analysis of a tunnel face.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author(s)** & **Year** & **Number of Data** & **\(L/D\)** & **\(H/D\)** & **Notation** \\ \hline \hline [PERSON] [5] & 1979 & 4 & 0 & 1–3.5 & centrifuge model tests \\ [PERSON] and [PERSON] [4] & 1999 & 4 & 0 & 0.25–1.5 & in situ tests \\ [32] & 1973 & 8 & 0–0.4 & 0.8–2.9 & in situ tests \\ [PERSON], et al. [17] & 2017 & 5 & 0 & 1–5 & finite element method \\ Present study & 2021 & 5 & 0 & 1–5 & FELA \\ \hline \hline \end{tabular}
\end{table}
Table 2: Database of the tunnel stability number (\(0\leq L/D<\ 0.5\)).
###### Abstract  
Crop yield prediction is essential for tasks like determining the optimal profile of crops to be planted, allocating government resources, effectively planning and preparing for aid distribution, making decisions about imports, and so on. Crop yield prediction using remote sensing data during the growing season is helpful to farm planning and management, which has received increasing attention. Information mining from multichannel geo-spatiotemporal data brings many benefits to crop yield prediction. However, most of the existing methods have not fully utilized the dimension reduction technology and the spatiotemporal feature of the data. In this paper, a new approach is proposed to predict the yield from multispatial images by using the dimension reduction method and a 3D convolutional neural network. In addition, regions with similar crop yields should have similar features learned by the network. Thus, metric learning and multitask learning are used to learn more discriminative features. We evaluated the proposed method on county-level soybean yield prediction in the United States, and the experimental results show the effectiveness of the proposed method. The proposed method provides new ideas for crop yield estimation and effectively improves the accuracy of crop yield estimation.  
crop yield prediction; metric learning; 3D convolutional neural network; feature constraint; multitask learning +
Footnote †: journal: _Applied_  
_aciences_
Applied sciences  
Optimization of a T-Shaped MIMO Antenna for Reduction of EMI  
[PERSON] 1\({}^{\text{\textregistered}}\), [PERSON] 2\({}^{\text{\textregistered}}\), [PERSON] 1,3,4,5,*, [PERSON] 6 and [PERSON] 7  
Center for Reliability Sciences & Technologies and Electronic Engineering Department, Chang Gung University, Taoyuan 33302, Taiwan; [EMAIL_ADDRESS]  
Center for Reliability Sciences & Technologies, Chang Gung University, Taoyuan 33302, Taiwan; [EMAIL_ADDRESS]  
Institute of Radiation Research, College of Medicine of Chang Gung University, Taoyuan 33302, Taiwan  
Center of Reliability Engineering, Ming Chi University of Technology, New Taipei City 24301, Taiwan  
Department of Urology, Chang Gung Memorial Hospital, Linkou, Taoyuan 33302, Taiwan  
Electronics and Communication Department, DIT University, Dehradun, Uttarakhand 248009, India; [EMAIL_ADDRESS]  
Department of Engineering, Lexorbis Consulting Private Ltd., New Delhi 110001, India; [EMAIL_ADDRESS]  
Correspondence: [EMAIL_ADDRESS]  
Received: 7 February 2020; Accepted: 27 April 2020; Published: 29 April 2020  
**Featured Application: A four-element wideband multiple-input multiple-output (MIMO) antenna is optimized using a genetic algorithm (GA). The antenna consists of a stub, four reduced ground planes, and four T-shaped radiating elements. The behavior of the antenna is analyzed with the following parameters: return loss, electromagnetic interference (EMI), and operating frequency. The antenna design is applicable to different applications. A 2\({}^{\text{K}}\) factorial design is used to identify the key design parameters responsible for affecting the above-mentioned performance indexes of the antenna. A GA optimization technique is utilized to increase the frequency range, where the return loss is less than \(-10\) dB, while its EMI is reduced to within a \(1\) m sphere of radiation, so as to reduce the threat of the antenna to nearby devices. The increased frequency range also makes it suitable for various applications such as satellite communication, imaging, and radar communication.**  
In this paper, optimization of a miniaturized multiple-inputs multiple-outputs (MIMO) antenna was performed. This antenna was composed of a T-shape radiating element with stub and reduced ground plane and a compact size of \(25\) mm \(\times\) 25 mm \(\times\) 1.6 mm. The behavior of antenna was evaluated in terms of return loss (S-parameter \(<-10\) dB), electromagnetic interference (EMI), and operation frequency. The antenna design is applicable to many applications. A 2\({}^{\text{K}}\) factorial design combined with a genetic algorithm (GA) optimization technique were used to identify the key design parameters responsible for affecting the performance quality of the antenna. Optimization of the antenna design for EMI reduction was utilized, and the optimal design showed enhanced bandwidth of the antenna and reduced power consumption.  
**Keywords:** factorial design of experiment; genetic algorithm optimization; return loss; electromagnetic interference; multiple-input multiple-output (MIMO)
## 3 Regression Analysis of Price (USD/MWh) Data for West, North, and Houston Regions in ERCOT
### Analysis of Hourly Models for North and Houston Price Data
As seen above, this has implications for the MOE, since, in terms of economic dispatch, less expensive renewable energy can be passed on to the final consumer of electricity in the form of lower prices, especially when the demand for electricity increases. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Skew-t Regression** & **Hour 3 AM** & **Hour 11 AM** & **Hour 4 PM** \\
**Independent Variables** & **(1)** & **(2)** & **(3)** \\ \hline WSapp18 & \(-0.0231\)* & \(-0.0380\)* & \(-0.0622\)* \\  & (0.0033) & (0.0055) & (0.0067) \\ Nprice1 & \(-2.7302\)* & - & - \\  & (0.0097) & - & - \\  & 0.6184 * & & \\  & (0.0208) & - & - \\  & & 0.8666 * & \\  & & (0.0193) & - \\  & & & 1.3495 * \\  & - & - & (0.0198) \\ Ln(NGprice) & 0.6267 * & 0.4966 * & 0.4229 * \\  & (0.0109) & (0.0123) & (0.0141) \\ Ln(Wgen) & \(-0.0965\)* & \(-0.0917\)* & \(-0.0931\)* \\  & (0.0023) & (0.0021) & (0.0029) \\ Ln(Sgen) & - & 0.0031* & 0.0239* \\  & (0.0023) & (0.0029) \\ Ln(Ngen) & \(-0.1592\)* & \(-0.1124\)* & \(-0.1188\)* \\  & (0.0083) & (0.0105) & (0.0133) \\ Ln(Nload) & 0.2318 * & 0.3850 * & 0.3935 * \\  & (0.0151) & (0.0232) & (0.0252) \\ Ln(REload) & 0.0045 * & 0.1877 * & 0.5529 * \\  & (0.0197) & (0.0262) & (0.0308) \\ Constant & 2.1950 * & \(-0.9020\)* & \(-4.1079\)* \\  & (0.1001) & (0.1218) & (0.1475) \\ \hline \hline \end{tabular} Legend: * \(p<0.0001\).  
\end{table} 
Table 5: North region skew-t estimates.
# A Dynamic Risk Assessment and Mitigation Model
## 5 Case Study  
Although the scenario used for demonstration purposes is fictional, it aims to represent a realistic environment of a small business, which includes a Virtual Private Network (VPN) for secure remote access, a public-facing website, a back-end database for storing customer data, product inventory, and transactions, as well as workstations (Figure 4). The vulnerabilities selected for this case study correspond to real CVEs that have been identified in similar components. In the proposed scenario, the VPN is vulnerable to CVE-2019-11510, which allows an unauthenticated remote attacker to exploit a specially crafted URI, enabling unauthorized file access that can be leveraged for authentication bypass. The Web Server (WebS) is affected by CVE-2019-0211 which could lead to attackers to execute arbitrary code. Additionally, the WorkStations (WS) is susceptible to CVE-2017-0143 and CVE-2017-8692, both of which facilitate remote code execution. Finally, the database (DB) is vulnerable to CVE-2021-41773, which could also lead to remote code execution. The network topology of this case study assumes that the VPN and WebS are accessible externally and both communicate with the router, which connects to the WS, and subsequently, the WS communicates with the DB.  
Figure 4: Experimental environment.
## 3 Results and Discussion
### Mechanical Properties
Nevertheless, the absence of interface failure in the air-quenched and HIP samples demonstrated a robust joint strength and revealed that both posts were properly selected. Fracture surfaces of the samples revealed signs of ductile failures (see Figure 3a-c), while R\({}_{\text{p0.2}}\), R\({}_{\text{m}}\), and A for the air-quenched samples were lower compared to the results achieved after the HIP treatment. The lower strength values could be attributed to (a) reduction of dislocation density, after the HT and (b) the \(\sigma\) phase precipitation, which is known to decrease the toughness and/or elongation and thus introduces embrittlement. According to the literature, there are methodologies to predict the \(\sigma\) phase, based on the chemical composition of the samples [59]. In particular,  
Figure 3: Analysis of mechanical properties. (**a–c**) A representative cup and cone fracture surface of the HIP sample showing clearly the dimples, with (K) aligned microductilities and (J) intergranular cracks; (**d,e**) a representative fracture surface of an air-quench and as-built sample, respectively; (**f,g**) bar charts with the tensile properties for the three sets of samples. the results in Table 1 were elaborated and an empirical formula was employed to examine the precipitation behaviour of the \(\sigma\) phase, as expressed in Equation (1):  
\[\text{Ratio Factor (Rf)}=(\%\text{Cr}-16\%\text{C})/\%\text{Ni} \tag{1}\]  
As stated by other researchers, if Rf is higher than 1.7, the \(\sigma\) phase can precipitate in stainless steel [59]. Therefore, there is a higher probability of the \(\sigma\) phase precipitation in the air-quenched sample since Rf is higher than 1.7, i.e., 2.21. Regarding the HIP samples, Rf is 0.63 and thus this is another evidence supporting the results obtained with the metallurgical analysis that revealed the \(\sigma\) phase precipitation tendency for all three sets of samples. The tests revealed that the HIP samples were superior to their as-built and conventionally heat treated counterparts in regards to their strength. Their higher \(\text{Rp}_{0.2}\) values (\(\sim\)300 MPa) can be explained with the minimised porosity (see Figure 2), the dislocation density movement within the lattice and also the deformation and recrystallisation which is facilitated by high temperature and higher pressure in HIP [60]. The results of the tensile tests also bare a good comparison with those reported in the literature and also matching the values reported for non-irradiated room temperature tensile tests [61; 62; 63].
###### Abstract  
In the long-hole stage open stoping with subsequent backfill mining of underground metal mines, the selection or optimization of stope dimension parameters is significant for safe and economic mining operations. To analyze the optimal stope sizes, the Mathews empirical graph method and FLAC3D numerical method can be used, but the analyzed safety results of the two methods are generally independent from each other. More importantly, economic indicators including production capacity and mining costs should be considered simultaneously to optimize the stope dimension which was mostly ignored in previous reports. In this paper, a new CRITIC-GRA model was proposed for the first time to build up a multi-factor quantitative optimization for stope dimension, which allows for a comprehensive analysis with preset influential safety and economic indicators. The indicators considered include the safety indicators such as stability probability for the side walls and roof of the open stope via the updated Mathews graph method, maximum displacement, plastic zones volume and maximum principal stress via FLAC3D simulations, as well as economic indicators such as mining costs and stope production capacity in mine operations. The model was then illustrated in an underground iron mine. With the given rockmass quality in the mine, the overall stability of the open stope can be improved instead of reduced to enlarge the single stage stope height (60 m) to a double stage height (120 m) by reducing the stope width from 20 m to 15 m, thereby significantly increasing the mineable ore amount and improving the stope safety. An integrated evaluation of open stope stability, mining capacity and costs objectively determined that scheme No. 10, with a slope length of 50 m, a width of 15 m and a height of 120 m, was the optimum out of the 20 preset schemes. The new CRITIC-GRA model offers a dependable reference tool for determining the optimal stope dimensions in similar underground mines.  
open stoping; stope dimension optimization; CRITIC; grey relational degree; FLAC3D; stability analysis; stope production capacity; mining cost
## 2 Hardware Overview and Experimental Setup
(**b**) Block diagram of the experiment: the Forward message (in TX1/RX2 block) is fed into the head-light thanks a current modulator, which modulates the light with a Manchester encoding. The light travels through the optical channel and it is collected by a photoreceiver (in TX2/RX1 block). The collected analog signal is sent to a comparator/digitizer board, and the Arduino based microcontroller performs a byte-wise comparison between a pre-stored message and the resulting digital signal. If no wrong bytes are detected in a packet, the microcontroller counts it and generates a backward message, which is fed into the rear-light. Analogously to the forward message, the backward message travels back from the TX2/RX1 to the TX1/RX2, and it is compared with the stored reference message. The PER is measured as the ratio between the wrong packets received at the end of the round-trip and the number of sent packets (\(10^{4}\)), for distances up to \(27\,\mathrm{m}\). The latency could be defined as the time interval elapsed between the first bit of the transmitted packet and the last bit of the received packet after a round trip. As pointed out by Figure 2a, the TX1 transmits \(10^{4}\) packets spaced by an _Inter-Packet Delay_ (IPD) slightly greater then the _Packet Time_ (PT) along the \(x\)-axis direction towards the second motorbike (TX2/RX1). If a perfect reception occurs, the TX2 replies back to the first motorbike and, again in the case of perfect reception, we find the minimum latency \(L_{0}\). A Time Division Multiplexing (TDM) is used so that each return packet from TX2/RX1 is sent back to the first motorcycle (TX1/RX2) during the IPD occurring between two packets in the forward packet stream transmitted by TX1/RX2. In a bidirectional communication, the loss of a packet can occur both in the first transmission (Figure 2b) and in the second transmission (Figure 2c). In both cases, the latency grows as \(L_{N}=L_{0}+N_{Lost}(IPD+PT)\), where \(N_{Lost}\) is the number of consecutively lost packets in RX2 after a round trip. In safety-critical applications, the characterization of the clustering of lost packets is a fundamental aspect [30; 31], as it determines the most probable latency value (_Statistically Averaged Latency_--SAL) corresponding to a certain PER in the communication. Indeed, assuming to have a data stream with a certain PT, IPD and PER = 0.5, several situations could arise depending on the size of error packets clusters and their occurrence. For example, the case where good packets and bad packets are evenly alternating is very different from the case where an initial perfect reception of packets is followed by a continuous stream of error packets, forming a very large error cluster. Even intuitively, these cases could tremendously differ in terms of expected latency.
# Cooperative Power-Domain NOMA Systems: An Overview
## 3 Types of NOMA  
NOMA is emerging as a key enabler for future-generation wireless networks, and hence many forms have been proposed and studied. Multiple users broadcast information on shared resources in all NOMA schemes, and multiple users can use the same physical channel, allowing for significant capacity gains. The receiver adopts a joint detection method such as SIC or message passing algorithm (MPA) to identify non-orthogonal signals. While the fundamental ideas of all NOMA schemes are the same, they are divided by the method of how non-orthogonality is accomplished. An overview of MA techniques is depicted in Figure 6.
# Comparing PlanetScope and Sentinel-2 Imagery for Mapping Mountain Pines in the Sarntal Alps, Italy
## 4 Discussion
### Feature Selection and Importance
While we used LiDAR metrics only as an additional optional feature to test its added value, future research could also assess LiDAR metrics features of globally available missions (e.g., NASA's Global Ecosystem Dynamics Investigation (GEDI) [58]) without computing textural features.
# Energy Transition on Sifnos: An Approach to Economic and Social Transition and Development
## 5 Extensions
### Rational Use of Energy  
The electricity production and storage from the hybrid power plant, as well as the transition to e-mobility and the aforementioned accompanied activities, will inevitably be combined with parallel energy saving actions, through calls for funding, periodically available both for public-municipal and private properties.  
In insular communities in Greece, for example, more than 80% of the existing building stock is evaluated at energy performance rank D or lower [69, 70, 71]. This means that with the introduction of the appropriate energy performance upgrade measures, annual energy savings in the range of 40-60% can be achieved in buildings alone [4, 5, 72]. Equal energy savings can be achieved in potable water production and supply facilities [73, 74].  
A crucial issue for the successful and adequate operation of the hybrid power plant is the peak demand drop and the flattening of the daily demand profile. This can be achieved with the introduction of demand-side management (DSM) essential strategies, such as decentralized power production and storage, integrated within a new smart grid layout [75, 76]. In Figure 18, the outcome of the introduction of small photovoltaic stations, for example in buildings' roofs, and small electrochemical storage units is presented. It was assumed that 200 families on Sifnos installed a 3 kW photovoltaic station each, while 100 families installed one storage device with a storage capacity of 10 kWh and a charge-discharge capacity of 10 kW. Electricity storage during the low demand period is performed with these decentralized storage devices, in order to be injected back in the grid during the peak demand period. The result of this combined decentralized power production and storage process is the so-called \"peak demand shaving\" (power demand transfer from the peak period to off-peak periods).  
The decentralized electricity production from photovoltaics exhibits a payback period of 5-6 years, calculated on the basis of the avoided electricity procurement cost (with an assumed electricity price of EUR 0.17/kWh). However, the installation of decentralized electricity storage devices for peak demand shaving does not today constitute an economically viable option, due to their high procurement price and short lifetime (for lithium-ion batteries, indicatively USD 400/kWh of storage capacity and 10 years, respectively). Based  
Figure 18: Peak demand shaving on Sifnos with decentralized electricity production and storage.  
on the assumption of a probable future drop in these prices in the range of EUR 100/kWh, then if a profit of USD 0.09/kWh is gained for each injected kWh of electricity in the insular grid during the peak demand period, and the investment will exhibit a payback period of 5 years, and hence it can be considered as an economic feasible perspective.  
The overall energy transition plan with the developmental perspectives analyzed previously are graphically summarized in Figure 19.
# Integrating Blockchains with the IoT: A Review of Architectures and Marine Use Cases
## 4 Applications and Challenges of Blockchains in MIoT and IoUT
### Challenges in Blockchain-Enabled MIoT and IoUT Systems
Additionally, the wrong choice of consensus mechanisms can hinder scalability, as storing information on IoUT nodes with restricted space becomes increasingly difficult as the network grows. Along those lines, these systems can further amplify the energy consumption challenge if it is not addressed effectively. Each transaction demands substantial energy, and long delays can occur before it is confirmed [17]. Additionally, the blockchain's high energy consumption and the potential for communication failures due to environmental factors present major concerns for deploying scalable and secure IoUT solutions. The harsh marine environment demands high reliability, and the hardware constraints of underwater sensors further exacerbate these challenges [20]. Further complicating the integration of blockchains with the IoUT is the challenge of migrating from legacy systems. The transition to BIoT systems is complex and costly, requiring specialized skills and expert handling. Thus, developing tools for building blockchain ecosystems that can efficiently replace the existing infrastructure is essential. At the same time, the cost associated with deploying underwater sensors is also high, making it difficult to manage these expenses while ensuring that IoUT solutions remain reliable and effective [20]. Lastly, as [PERSON] et al. [24] reveal, data sharing within the MIoT/IoUT also faces obstacles due to the lack of trust among edge computing participants and concerns regarding user privacy. More specifically, the authors argue that effective data sharing is difficult when communication between nodes is unreliable, and users may be hesitant to share data when anonymity is not guaranteed. Furthermore, factors such as network instability (e.g., frequent disconnections and inconsistent sensor throughput) can further complicate the effective management of data in these systems [14]. Figure 7 illustrates the various challenges faced by MIoT and IoUT systems, both in terms of the inherent difficulties posed by underwater environments and the additional complexities introduced by the integration of blockchain technology.