esa-sceva/satcom-chunk-collection huggingface dataset:
# Continuous-Wave THz Imaging for Biomedical Samples
## 5 The Application of CW Imaging with Biological Tissues
### Bones  
The skeleton of most vertebrates is composed of hard bones, which not only support and protect the vital organs in the body but also play an important role in maintaining normal life activities. The imaging principle and setup of the CW THz-CT imaging technology have been described in Section 4, and this method can be applied to 3D imaging the biological hard tissues such as teeth and bones. The effect of water absorbing the THz waves becomes less because of the low water content of hard tissues, so clear structural information of dehydrated tissue can be obtained by CW THz-CT imaging technology. In 2012, [PERSON] et al. used 0.3-2.75 THz to image a dry human lumbar vertebra and skull, indicating that dense bones had higher THz absorption at 110 GHz than spongy bones. THz imaging provides many similarities with real objects [96]. The THz images could easily obtain abundant information on the absorption, refractive index, and the density distribution of bone tissues. The chicken ulna is composed of the diaphysis and proximal epiphysis. The interior proximal epiphysis mainly includes spongy bone with a dense grid shape. From the outside to the inside of the two-dimensional cross-section view, the proximal epiphysis is respectively composed of the periosteum, compact bone, spongy bone, and bone marrow. In 2018, [PERSON] et al. combined CW THz technology with CT to image the internal structure of a dry chicken ulna; the schematic of the THz-CT imaging system is shown in Figure 12. They obtained two-dimensional cross-sectional images with different diameters of five positions of the sample [97]. The backward-wave oscillators and a Golay cell detector were utilised to build the imaging system. Furthermore, they used the filtered back projection (FBP) algorithm to reconstruct the two-dimensional cross-sectional images of the samples. Position I and position II were located in the backbone of the chicken ulna, as shown in Figure 16a. There was an irregular pattern of light and shade in the central part of the cross-sectional THz images, as shown in Figure 16c,e, because the liquid bone marrow and spongy bone marrow in the marrow cavity had different absorption coefficients for THz radiation in dry samples. The reconstructed cross-sectional images and the change of absorption coefficient from position I to position V indicated that the closer to the proximal epiphyseal bone, the smaller the marrow cavity. A brief overview of the data is given in Table 1. It shows the CW THz imaging studies with biomedical samples that have been conducted in the last 10 years. Figure 16: The reconstructed image of the chicken ulna obtained with CW THz-CT: (**a**) Schematic diagram and sketch image of 2D cross-sectional image of the chicken ulna; (**b**,**d**) Photos of sections, II and IV of the chicken ulna; (**c**,**e**) The corresponding 2-D cross-sectional images [97]. Copyright © 2018, Society of Photo-Optical Instrumentation Engineers (SPIE).
# Impact of CoAP and MQTT on NB-IoT System Performance +
## 2 Performance Evaluation
### NB-IoT Modeling  
The used simulator models NB-IoT protocol stack in high detail, spanning from Radio Resource Control (RRC) protocol layer in the control plane, and Packet Data Convergence Protocol (PDCP) layer in user plane down to MAC and physical layers. This includes detailed modelling of RRC, PDCP, Radio Link Control (RLC) and MAC layers, and implementation of relevant NB-IoT physical channels (NPRACH: Narrowband Physical Downlink Control Channel, NPDCCH; Narrowband Physical Downlink Shared Channel, NPDSCH; NPUSCH). These protocol stacks are implemented both in the base station and the device, and the NPDSCH and NPUSCH physical channel model noise and interference coming from neighbor cells and from the transmissions of other devices, while other physical channels are more simplistic and implement probability-based errors.  
The propagation model for the radio channels follow the 3 GPP typical urban channel model [14], where the generated devices are indoors resulting in 20 dB additional attenuation. The NB-IoT carrier in the simulations is deployed in 900 MHz carrier. The maximum transmission power of the base stations is 40 W, and base stations have 2 receive/2 transmissions antennas, while the devices have maximum transmission power of 0.2 W and 1 receive/1 transmission antenna. The scheduling algorithm used by the base stations is round-robin.  
Three coverage enhancement (CE) levels are configured with certain repetition configuration on each of the CE-level, with Maximum Coupling Loss (MCL) thresholds determining on which CE-level the device should perform random access and consequently, the data transmission.
## 2 Calculation Theory Revision
### Beam-Spring Method
#### 2.1.2 Apply the Horizontal Distributed Forces  
If only the horizontal distributed force \(q(y)\) is applied, the structural analysis diagram is shown in Figure 3, and the included angle between the element and horizontal force is \(\pi-\theta_{1}\).  
The laterally distributed force \(q(y)\) is equivalent to horizontal and vertical component forces to calculate the equivalent node forces. The derivation is shown as:  
\[\begin{array}{l}N_{1}^{q}=\int_{y_{1}}^{y_{2}}q(y)\cos(\pi-\theta_{1})A_{1}( \frac{y-y_{1}}{\sin(\theta_{1})})dy\\ Q_{1}^{q}=\int_{y_{1}}^{y_{2}}q(y)\sin(\pi-\theta_{1})H_{1}(\frac{y-y_{1}}{\sin (\theta_{1})})dy\\ M_{1}^{q}=\int_{y_{1}}^{y_{2}}q(y)\sin(\pi-\theta_{1})H_{2}(\frac{y-y_{1}}{\sin (\theta_{1})})dy\\ N_{2}^{q}=\int_{y_{1}}^{y_{2}}q(y)\cos(\pi-\theta_{1})A_{2}(\frac{y-y_{1}}{\sin (\theta_{1})})dy\\ Q_{2}^{q}=\int_{y_{2}}^{y_{2}}q(y)\sin(\pi-\theta_{1})H_{3}(\frac{y-y_{1}}{\sin (\theta_{1})})dy\\ M_{2}^{q}=\int_{y_{1}}^{y_{2}}q(y)\sin(\pi-\theta_{1})H_{4}(\frac{y-y_{1}}{\sin (\theta_{1})})dy\end{array} \tag{4}\]  
The equivalent node force of node 1 and node 2 in the local coordinate system is:  
\[\begin{array}{l}\left\{F_{1}^{\rm L}\right\}=\left\{\begin{array}{l}N_{1 }\\ Q_{1}\\ M_{1}\\ N_{2}\\ Q_{2}\\ M_{2}\end{array}\right\}=\left\{\begin{array}{l}N_{1}^{p}\\ Q_{1}^{p}\\ M_{1}^{p}\\ N_{2}^{p}\\ Q_{2}^{p}\\ M_{2}^{p}\end{array}\right\}+\left\{\begin{array}{l}N_{1}^{q}\\ Q_{1}^{q}\\ M_{1}^{q}\\ N_{2}^{q}\\ M_{2}^{q}\end{array}\right\}\end{array} \tag{5}\]  
where \(F_{1}^{\rm L}\) is the equivalent node force of node 1 in the local coordinate system, \(F_{1}^{\rm L}=\left[N_{1}\ Q_{1}\ M_{1}\right]^{T}\) ;\(F_{2}^{\rm L}\) is the equivalent node force of node 2 in the local coordinate system, and \(F_{2}^{\rm L}=\left[N_{2}\ Q_{2}\ M_{2}\right]^{T}\).  
The stiffness matrix of the beam in global coordinate is:  
\[\left[K^{1,G}\right]_{6\times 6}=\left[\begin{matrix}k_{11}^{1,G}&k_{12}^{1,G}\\ k_{21}^{1,G}&k_{22}^{1,G}\\ k_{21}^{1,G}&k_{22}^{1,G}\end{matrix}\right]=[T]\left[\begin{matrix}k_{11}&k_{ 12}\\ k_{21}&k_{22}\end{matrix}\right][T]^{T} \tag{6}\]  
Figure 3: Free body diagram.  
where \([k_{11}]\), \([k_{12}]\), \([k_{21}]\), and \([k_{22}]\) are nodal stiffness matrices, according to beam-spring model researches [20]. \([T]\) is the coordinate transformation matrix:  
\[[T]=\begin{bmatrix}T_{1}&\\ &T_{2}\end{bmatrix},[T_{i}]=\begin{bmatrix}\cos\alpha_{i}&-\sin\alpha_{i}&0\\ \sin\alpha_{i}&\cos\alpha_{i}&0\\ 0&0&1\end{bmatrix}(i=1,2) \tag{7}\]  
where \(\alpha_{i}(i=1,2)\) is the angle between the arc tangent direction of nodes 1 and 2 and the X-axis. For the straight beam model, \(\alpha_{1}=\alpha_{2}\).
## 4 Experiments
### Application and Datasets
#### 4.2.1 Daily and Sports Activities Dataset  
DSA comprises data from nine sensors placed at five different body parts of eight users for 5 min per activity. The 5 min signals are divided into 5 s segments so that 480 (=60 \(\times\) 8) signal segments were obtained for each activity. A total of 45 signals with 25 features extracted every 5 s yield a 5625-dimensional feature vector. Some of the activities include sitting, standing, lying on the back and right side, ascending and descending stairs, cycling on an exercise bike in horizontal and vertical positions, rowing, and so on. It is a 19-category and well-balanced dataset.
# Statistical Analysis and Modeling for Optical Networks
## 4 A Brief Introduction to Optical Networks
### Components of Optical Networks  
Optical networks are high-speed communication systems that use light to transmit data over fiber-optic cables. The key components of these networks include optical fibers, optical transmitters, and optical receivers [1]. Optical fibers, typically made of silica or plastic, serve as the transmission medium, offering low signal attenuation and high bandwidth. They are classified into single-mode and multi-mode fibers, with single-mode being ideal for long-distance communication due to lower dispersion. Optical transmitters, usually laser diodes or LEDs, convert electrical signals into optical signals. These signals travel through the fiber and are received by optical receivers, which consist of photodetectors that convert the light signals back into electrical form [1]. These core components enable efficient data transmission, making optical networks a backbone for modern communication systems. In addition to the fundamental components, optical networks rely on several  
Figure 1: Opaque and transparent switching in optical networks. In transparent switching, the signal remains as light throughout. However, in opaque switching, O/E and E/O conversions are needed.  
other components, which can be broadly divided into two types: node-related components and link-related components [3]. These components enable scalable and high-speed communication, making optical networks essential for long distance communications, data centers, and cloud computing [47].  
Optical networks incorporate advanced control and management systems to optimize efficiency and reliability. Software-defined networking (SDN) enables dynamic control of network resources, ensuring optimal bandwidth allocation and reducing congestion [47]. Optical network monitoring systems continuously assess fiber health, detecting faults and signal degradation to prevent downtime. In addition to that, protection and restoration mechanisms, such as ring and mesh topologies, ensure network resilience against failures by rerouting traffic in case of disruptions [1]. As optical networks continue to evolve, emerging technologies, like photonic integrated circuits and quantum communication, promise even higher data rates, enhanced security, and improved energy efficiency [80]. These innovations solidify optical networks as the backbone of future high-speed global communication infrastructure. The node-related components are either placed in the nodes, or they directly depend on the features of the nodes [3]. The link-related components are either placed along the links, or they directly depend on the features of the links [3]. Both the node-related components and link-related components are equally important for the overall success of the optical networks.
# The Rapid Establishment of Large Wind Fields via an Inverse Process
## 2 Methods
### Traditional POD and Gappy POD
This approach is based on the capability of the gappy POD method in recovering missing data during the reconstruction process, with the consideration that the missing data are outside the original domain. The new algorithm is formed by the following sequence:  
* A series of velocity distribution profiles for a large area is generated by offsite CFD simulations \(\left\{\mathbf{U}^{I}\right\}_{l=1}^{m}\). * A sub-domain \(\left\{\mathbf{U}^{I}\right\}_{l=p}^{q}\), i.e., a small area such as a wind farm within the large area, is selected, and a group of snapshots of wind velocities over this subset of the large area is obtained. A group of basis vectors \(\mathbf{\Phi}\) of the snapshots \(\left\{\mathbf{U}^{I}\right\}_{l=p}^{q}\) is extracted from the subset domain by Equation (2). * Wind speed is measured at a small number of selected locations as the real time input for the wind field reconstruction in the next step. * The wind velocity profile for the larger area is reconstructed with a refined mesh via gappy data reconstruction methods, using Equation (3) with the limited amount of real time data from the above step. The first and second steps of the above algorithm can be performed offsite, as wind forecasting is not involved. Therefore, the time consumption is not a concern, since no real time data are needed during these steps. The wind forecast is made in the third step. In the third step, only a single step matrix calculation is needed to obtain the velocity distribution. One of the essences of this method is that the dimension of the matrix can be several orders of magnitude lower than those used in the CFD calculations. This allows the wind velocity to be reconstructed very quickly, virtually in real time. In addition, there is no restriction in selecting the subset of the full area. The choice can be very flexible or specified by the operators of the windfarm. For example, if a wind forecast is to be made, the subset domain can be chosen at the wind farm itself. One way of doing so is to calculate the wind velocity distribution for the large area and then draw the velocity profile at the mesh points within this subset domain. This is, however, at this moment an intuition, and its performance will be examined in the following sections.
# Enhancing Absorption Bandwidth through Vertically Oriented Metamaterials
## 6 Control of Intra-Cell Coupling  
The ability to deactivate intra-cell coupling is of great interest, since this would increase both versatility of the DSRR geometry as well as control over the parameter space. Due to the high field concentration at the gaps in each resonator during peak absorption, three additional geometries were investigated, in which the VSRR and PSRR gaps were spatially separated. All three geometries and their optical responses are illustrated in Figure 7.  
In the first case, the VSRR was rotated about the \(y\)-axis by \(180^{\circ}\), placing the SRR gap near the top of the walls; the PSRR orientation remained constant. In this configuration, the absorption profile strongly resembles that of the single PSRR unit cell, indicating that intra-cell coupling between the SRRs can be deactivated. This is due to interactions of the SRR with the unit cell membrane and gold layers as a function of VSRR orientation. For example, if the VSRR in Figure 1 (right) is placed in the rotated \(180^{\circ}\) orientation, a much weaker absorption peak is produced (\(A<0.25\)). In the DSRR geometry, this enables the PSRR resonance to dominate the absorption response, yielding an absorption spectrum similar to Figure 3 (top).  
In the second case, the VSRR orientation remained unchanged, and the PSRR was rotated about the \(z\)-axis by \(180^{\circ}\). In this configuration, the absorption profile resembles that of the DSRR geometry in Figure 4. Although the spatial distance between the SRRs is unchanged, the coupling is strongly based on the presence of strong absorption in the VSRR.  
Figure 6: (**left**) The DSRR unit cell geometry is shown, also illustrating variation of the angle of incidence, \(\theta\). (**right**) Spectral absorption is plotted as a function of frequency and angle of incidence.  
In the third case, the VSRR was rotated about the \(y\)-axis by 180\({}^{\circ}\), and the PSRR was rotated about the \(z\)-axis by 180\({}^{\circ}\). In this configuration, the absorption profile resembles that of the single PSRR unit cell, again indicating that intra-cell coupling between SRRs has been deactivated.
## 4 Results
### Processing Prior to Transmission
#### 4.1.3 Performance Analysis of Processing Prior to Transmission  
The process of adapting the original image in Unity to the appropriate format for further processing by computer vision algorithms in C++ and Python demands different processing times. The time from capturing the rendered image to obtaining the image inthe appropriate format was measured to calculate these pre-transmission processing times. The result of this test is graphically shown in Figure 4.  
The figure shows the time spent to convert 300 images to the required format in C++ (represented in blue) and Python (represented in orange). As can be observed, the processing time required to convert the image in Unity into the appropriate format for submission to the C++ processing option was much longer than that required for the Python option in all cases. With much more variable processing times ranging from 35 to 57 ms, the data conversion process for C++ consumed on average about 40 ms. In the case of Python, more regular behaviour was observed with minimum processing time of 4 ms and maximum time of 17 ms, and an average of just over 6 ms.
## Materials and Methods
### Field Investigation  
In this study, a total of 250 plots were surveyed to assess the damage severity caused by crop diseases as ground truth data during 29 and 31 October 2017 (Figure 1). The sampling designs for each individual disease species were based on the Rules of Investigation and Forecast of Rice Blast (GB/T 15790-2009), Rules of Investigation and Forecast of Disease caused by Rice Black-Streaked Dwarf Virus (NY/T 2730-2015), and the Rules for Investigation and Forecast of the Rice Blight Virus (NY/T 1609-2008). Each site covered an area of 3 m \(\times\) 3 m and the plots were compared with corresponding image pixels. A visual discrimination method was applied to assess the infestation status and damage severity for each plot because some rice diseases occurred simultaneously in the study area. The plots were labeled as healthy if they had not been infested or if the proportion of damaged leaves was less than 10%; for rice leaves infected with mixed infestations were defined as the corresponding disease with the most dominant proportion of the infestation. Figure 2 shows a representative example for healthy samples and other samples infected with rice dwarf, rice blast, and glume blight disease, and the corresponding PL multi-spectral reflectance curves. In this study, 70% of the plots were randomly selected for model calibration and the remaining 30% were used for validation.
## 4 Models and Algorithm Design
### Representation of the Renewable Resources
#### 4.1.2 Wind Power Plants  
The output power \(P_{w_{i}}\) of the \(i\)-th wind power plant can be modeled as [27,50,59]:  
\[P_{w_{i}}=\frac{1}{2}\cdot(\rho_{\text{Air}}\cdot A_{i}\cdot u_{i}^{3}),\]  
where \(\rho_{\text{Air}}\) represents the air density. Its value is generally considered as 1 kg/m\({}^{3}\). Term \(A_{i}\) denotes the windswept area of the \(i\)-th agent (m\({}^{2}\)). This parameter accounts for the size of the rotor-swept area of the wind turbine. Parameter \(u_{i}\) represents the velocity of the wind around the \(i\)-th agent (m/s). Wind speed is a critical factor in determining the power output of a wind turbine. The index \(i\) is given here because it can represent different-sized and powerful wind power plants. If it is necessary to calculate several calculations in a row, again the index \(k\) can be used--\(P_{w_{i}}(k)\).  
The cost function for wind farms can be defined as [10; 26; 28]:  
\[C_{w_{i}}(P_{w_{i}})=\alpha_{w_{i}}P_{w_{i}}, \tag{17}\]  
where the \(\alpha_{w_{i}}\) denotes the same as for solar power plants [10].  
The price function form can be designed in more investment-based view again [26; 55]:  
\[C_{w_{i}}(P_{w_{i}})=a_{w}I^{P_{w}}P_{w_{i}}+G^{E_{w}}P_{w_{i}}. \tag{18}\]  
Variables used in (18) denote the same variables as for solar power plants Section 4.1.1, but here they are listed with the index \(w\) for wind power plants.  
But even in this case, the price function can be defined as zero [8]:  
\[C_{w_{i}}(P_{w_{i}})=0. \tag{19}\]  
Moreover, that is because wind costs nothing as an energy source [54; 57].  
When renewables have non-zero costs, the model will consider the economic factors in the dispatch decisions. The dispatch algorithm will prioritize sources with lower associated costs to meet the demand. In certain situations, it might result in a non-dispatch of renewable sources, even if they are available if other sources are more economically favorable. Regarding the potential for curtailment, it is indeed a consideration when integrating renewable energy sources into the energy system. Curtailment might be necessary if the battery storage is full, demand is low, and there is an excess of renewable generation due to favorable weather conditions. Curtailment involves intentionally reducing or restricting the output of renewable sources to balance supply and demand [60; 61]. In a realistic model, including curtailment scenarios would allow for a more accurate representation of the challenges associated with renewable energy integration. Factors such as energy storage capacity, grid flexibility, and market conditions need to be considered to optimize the utilization of renewable energy and minimize curtailment.
## 4 Results
### Accurate pH Measurements for Global Industry 4.0 and Vertical Farming Applications  
In order to validate the accuracy of our portable instrumentation, as intended for global VF (and _Industry 4.0_) applications, a laboratory grade pH device (Ohaus Corporation, Parsippany, NJ, USA, AB33M1) was selected as gold standard. Thirteen samples with varying pH values were utilized. For every measurement, a rigorous process involving cleanliness, temperature stability, calibration, and mechanical stability of the probe was implemented. Figure 7 depicts the results of the instrument presented in this work to enable accurate pH measurements for global _Industry 4.0_ and VF applications. In order to ensure global VF applicability, the cost of the device is important. In our case, the cost (uelectronics) of the instrument was ~60 USD. In contrast, the gold standard costs ~715 USD (Viresa); i.e., ~11.5\(\times\) more expensive. Thus, our solution is cost-effective, portable, and accurate.  
Using the information depicted in Figure 7, the average and median absolute deviations (reference minus measured pH) are 0.057 and 0.063 pH units, respectively. Meanwhile, the standard deviation of the absolute deviation is 0.03 pH units. For the acidic substance exhibiting the lowest pH value, the maximum relative error is 5%. In contrast, alkaline substances have relative errors that are as low as 0.3%. Figure 7 depicts error bars that describe the absolute deviations of measured and reference data. As noted before, various error bars are not easily distinguishable due to instrumentation performance. In any case, the accuracy and applicability of the proposed instrument is clearly established.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Phase II. Instrumentation \({}^{*}\)** \\ \hline
**pH** & **E[\(V\)] [mV]** \\ \hline
4 & 173.666667 \\
7 & 0 \\
10 & \(-\)171.666667 \\ \hline \hline \end{tabular} \({}^{*}\)Here, E[\(V\)] is the expected value of a random variable \(V\), which represents the measured voltage in mV.  
\end{table}
Table 2: Statistical sensitivity parameters derived from pH sensor characterization: Phase II.