esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Comparative Analysis  
The proposed antenna performance was compared with the existing designs in Table 2. The proposed antenna is relatively compact compared to most of the other antennas in the table. The antenna also has a comparatively optimal gain. The proposed antenna with the bottom watch case has the lowest SAR value. Most of the antennas in the table are planar antennas; however, due to partial ground/defected ground structure (to improve the bandwidth), the antenna structure replicates monopole behavior. As a result, these are labeled as monopole antennas. In our case, the antenna has a full ground plane depicting a perfect patch antenna. The proposed antenna's link budget was also analyzed, and it was  
Figure 21: Estimated link budget between the ideal dipole antenna and the proposed antenna at 50 cm distance for various rotational angles for the two bands. (**a**) 25.5 GHz and (**b**) 38 GHz.  
Figure 20: Link budget estimation environment under line-of-sight conditions.  
demonstrated that the antenna can deliver 1 Gbps of data at a 20 dB link margin, which other designs have not performed.
# A Comprehensive Survey on the Societal Aspects of Smart Cities
## 3 Smart Cities' Technologies
### Standards/Open Standards
It is for these reasons that open standards exist. Open standards are accessible to everyone and do not require payment. They are often, but not always, under an open license, allowing for anyone to further develop the standard. This openness can lead to faster development and access to more ideas but comes with the trade-off that there is less control, which makes coordination between different contributors and prioritization of ideas more challenging [212]. This can be both good and bad, as new ideas can lead to unexpected innovations that may not be pursued in a more formal environment. However, they can also cause a split between contributors, leading to the creation of separate standards. While competition breeds innovation, standards are meant to create a solution that can be used by everyone, and competing standards bring confusion to the market. This is particularly important in the case of smart cities, as not having access to a single set of standards creates a situation where each system uses its own technologies and does not allow for easy communication with other systems. In a smart city where countless systems are expected to be connected, this creates situations where, at best, the smart city is bogged down with middleware between systems, or, at worst, the smart city project is stopped before it is fully realized because it becomes too complex and expensive to implement. This also means that solutions implemented in one place cannot be easily reused or adapted elsewhere, or that a user is locked into a particular system, so upgrades are only possible via the system's vendor, or by throwing out the whole system and replacing it with a new one, which may have the same problem [213]. Open standards counter this, as they allow smaller companies to create systems without being locked out by a lack of money to pay for other standards, and those systems cannot be locked out of other companies' systems due to incompatibility. This allows for the creation of an interconnected IoT environment that is easy to upgrade and expand, very scalable, and cheaper than the alternative. It is important to note that even if an open standard is not completely open--certain parts are locked out, or only a select few can work on its development--if it allows open communication between systems and accessible data, then any system can be connected to another with minimal effort.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**+0\% BM**} & \multicolumn{2}{c}{**+5\% BM**} & \multicolumn{2}{c}{**+10\% BM**} \\
**Peak Values** & **Mean \(\pm\) SD** & **95\% CI** & **Mean \(\pm\) SD** & **95\% CI** & **Mean \(\pm\) SD** & **95\% CI** \\ \hline \(\dot{\mathrm{V}}\)O\({}_{2}\) (mL.min\({}^{-1}\).kg\({}^{-1}\)) & 59.4 \(\pm\) 6.7 & 55.7–63.1 & 59.1 \(\pm\) 5.7 & 56.0–62.3 & 57.0 \(\pm\) 5.5 & 54.0–60.1 \\ Power (W) & 338 \(\pm\) 27 & 323–353 & 337 \(\pm\) 32 & 320–355 & 346 \(\pm\) 35 & 327–366 \\ nPower (W-kg\({}^{-1}\)) & 4.6 \(\pm\) 0.4 & 4.4–4.9 & 4.7 \(\pm\) 0.3 & 4.5–4.9 & 4.8 \(\pm\) 0.4 & 4.6–5.0 \\ Speed (km.h\({}^{-1}\)) & 17.1 \(\pm\) 1.2 & 16.4–17.7 & 16.5 \(\pm\) 1.0 & 15.9–17.0 & 15.9 \(\pm\) 1.0 & 15.3–16.4 \\ RER & 1.09 \(\pm\) 0.08 & 1.04–1.14 & 1.08 \(\pm\) 0.06 & 1.04–1.12 & 1.07 \(\pm\) 0.09 & 1.02–1.12 \\ Lactate (mmol/L) & 11.4 \(\pm\) 4.1 & 9.1–13.6 & 11.0 \(\pm\) 3.4 & 9.1–12.8 & 12.6 \(\pm\) 4.6 & 10.1–15.1 \\ CoT (J-kg\({}^{-1}\).m\({}^{-1}\)) & 4.2 \(\pm\) 0.6 & 3.9–4.6 & 4.3 \(\pm\) 0.5 & 4.1–4.6 & 4.4 \(\pm\) 0.4 & 4.1–4.6 \\ \hline VT1 & & & & & & \\ \hline \(\dot{\mathrm{V}}\)O\({}_{2}\) (mL.min\({}^{-1}\).kg\({}^{-1}\)) & 35.1 \(\pm\) 3.9 & 33.2–37.4 & 34.3 \(\pm\) 3.4 & 32.5–36.6 & 34.2 \(\pm\) 3.9 & 31.8–36.0 \\ rVO\({}_{2}\) (\%) & 59.4 \(\pm\) 0.3 & 57.7–61.1 & 58.9 \(\pm\) 0.3 & 57.5–60.4 & 60.0 \(\pm\) 0.3 & 58.1–61.0 \\ Power (W) & 200 \(\pm\) 23 & 187–213 & 204 \(\pm\) 16 & 195–212 & 208 \(\pm\) 23 & 195–220 \\ nPower (W-kg\({}^{-1}\)) & 2.7 \(\pm\) 0.4 & 2.5–2.9 & 2.8 \(\pm\) 0.2 & 2.7–2.9 & 2.9 \(\pm\) 0.3 & 2.7–3.0 \\ Speed (km.h\({}^{-1}\)) & 9.5 \(\pm\) 0.9 & 9.0–10.0 & 9.2 \(\pm\) 0.6 & 8.9–9.5 & 8.9 \(\pm\) 0.8 & 8.4–9.3 \\ RER & 0.82 \(\pm\) 0.04 & 0.80–0.84 & 0.82 \(\pm\) 0.06 & 0.78–0.85 & 0.81 \(\pm\) 0.06 & 0.77–0.84 \\ CoT (J-kg\({}^{-1}\).m\({}^{-1}\)) & 4.5 \(\pm\) 0.6 & 4.2–4.9 & 4.5 \(\pm\) 0.4 & 4.3–4.8 & 4.7 \(\pm\) 0.6 & 4.3–5.0 \\ \hline VT2 & & & & & & \\ \hline \(\dot{\mathrm{V}}\)O\({}_{2}\) (mL.min\({}^{-1}\).kg\({}^{-1}\)) & 49.6 \(\pm\) 5.1 & 46.8–52.4 & 49.0 \(\pm\) 4.0 & 46.8–51.2 & 47.9 \(\pm\) 4.6 & 45.3–50.4 \\ rVO\({}_{2}\) (\%) & 83.6 \(\pm\) 0.3 & 81.9–85.3 & 83.7 \(\pm\) 0.3 & 81.9–85.6 & 83.3 \(\pm\) 0.3 & 81.5–85.2 \\ Power (W) & 277 \(\pm\) 27 & 263–292 & 278 \(\pm\) 20 & 268–289 & 291 \(\pm\) 17 & 282–300 \\ nPower (W-kg\({}^{-1}\)) & 3.8 \(\pm\) 0.4 & 3.6–4.0 & 3.8 \(\pm\) 0.2 & 3.7–4.0 & 4.0 \(\pm\) 0.3 & 3.9–4.2 \\ Speed (km.h\({}^{-1}\)) & 14.0 \(\pm\) 1.1 & 13.4–14.6 & 13.1 \(\pm\) 0.7 & 12.7–13.5 & 12.7 \(\pm\) 1.0 & 12.1–13.2 \\ RER & 0.94 \(\pm\) 0.03 & 0.93–0.96 & 0.93 \(\pm\) 0.05 & 0.90–0.96 & 0.96 \(\pm\) 0.0 & 0.91–0.99 \\ CoT (J-kg\({}^{-1}\).m\({}^{-1}\)) & 4.3 \(\pm\) 0.5 & 4.0–4.6 & 4.4 \(\pm\) 0.3 & 4.1–4.7 & 4.6 \(\pm\) 0.5 & 4.3–4.9 \\ \hline \end{tabular}
\end{table} 
Table 2: Physiological and mechanical responses (mean \(\pm\) SD, 95% CI) during an incremental running protocol under three different overload conditions (+0, +5 and +10% of body mass).
# Optimizing Healthcare Delivery: A Model for Staffing, Patient Assignment, and Resource Allocation
## 4 Methodology
### Research Input Data  
The model's ranges and variables were created using Python programming to describe the optimization problem components (Note: _In Python programming, indexing starts from 0 rather than 1_). For the two procedures, the ranges for staff members, time slots, patients, tasks, and resources represented in the first process are: \(N\) (1-21), denoting 20 members of staff (including _physicians_, _nurses_, _surgeons_, _pharmacists_, _medical technologists_, etc.); \(T\) (1-11), 10 time slots in hours; \(P\) (1-31), 30 patients; \(K\) (1-16), which represents 15 tasks (including _administering medication_, _conducting patient assessments_, _assisting in surgeries_, _monitoring vital signs_, _providing wound care_, _conducting diagnostic tests_, _assisting with patient mobility_, _offering emotional support_, _collaborating with healthcare professionals_, and _educating patients on disease management_); and \(R\) (1-11), which represents 10 allocated resources (including _hospital beds_, _operating rooms_, _medical equipment_, _laboratory facilities_, _radiology equipment_, _surgical instruments_, _medications_, _ambulances_, _intensive care unit (ICU) beds_, _anesthesia machines_, _patient monitoring systems_, _wheelchairs_, _surgical supplies_, _imaging and diagnostic equipment_, and _personal protective equipment (PPE)_. The ranges for the second process are \(N\) (1-31), \(T\) (1-6), \(P\) (1-21), \(K\) (1-11), and \(R\) (1-16).  
The parameters' dictionaries were generated using the predefined ranges for the two procedures. In the dictionary, \(I\) contained random integer values between 1 and 10 for each combination of staff members \(i\) and \(j\) and time slot. For every staff member \(i\) and time slot \(t\) combination, the dictionary \(W\) included random integer values between 1 and 10. Moreover, we further generated new dictionaries that represented the model's components. These dictionaries (\(C\), \(D\), \(Q\), \(O\), \(U\), \(S\), \(M\), \(H\), \(D_{t}\), and \(F\)) capture healthcare scheduling optimization model parameters and relationships. Afterward, these new dictionaries were populated with random integer values (\(C\) (1-10), \(D\) (1-10), \(Q\) (1-10), \(O\) (1-10), \(U\) (1-10), \(S\) (1-5), \(M\) (1-5), \(H\) (8-12), \(D_{t}\) (2-5), and \(F\) (1-3)) in preset ranges to simulate data. The model optimization utilized these input data from the inherent simulation to arrive at the optimal value and further analyze it. Hence, for the model, generating random values within the given ranges created a diverse and representative dataset covering healthcare scheduling constraints.
## 2 Materials and Methods  
The OFDM technique was implemented in a laboratory model of an acoustic data transmission system, designed at the Department of Sonar Systems, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology. The system enables the OFDM modulation parameters and ECC rate to be adjusted to the propagation conditions as to obtain the desired Bit Error Rate, which is less than \(10^{-4}\). Some of the OFDM signal parameters (the signal bandwidth \(B\), the carrier frequency \(f_{c}\), and the sampling frequency \(f_{s}\)) are fixed, and they result from the parameters of hardware components such as ultrasonic transducers or underwater telephones. However, two of the key OFDMsignal parameters, namely the symbol duration \(T_{OFDM}\) and the subcarrier spacing \(B_{OFDM}\), are chosen adaptively. Thus, the OFDM data transmission tests of the UAC system are preceded by measurement of the channel's impulse response and estimation of its transmission parameters, which makes it possible to match the values of \(B_{OFDM}\) and \(T_{OFDM}\) to the specific propagation conditions.
# Influence of Roll Diameter on Material Deformation and Properties during Wire Flat Rolling
## 2 FE Analysis and Model Validation
### Model Validation  
During the general forming process, the FE model is typically validated by comparing the external imposed force for the deformation. In this study, since shape control is a large issue in the wire flat rolling industry, the shape changes between the experimental and simulated results were compared to validate the results of the FE analysis. Figure 4a presents the \(b\) and \(W\) of the simulated results with \(R_{\text{h}}\), that is, 10%, 20%, 30%, and 40%, at a roll diameter of 400 mm. Both \(b\) and \(W\) increased with \(R_{\text{h}}\). Figure 4b compares \(W\) and \(b\) from the FE analysis with the experimental results. The values from the FE analysis were slightly lower than those of the experiment. The authors believe that the deviation in the numerical and experimental values was primarily associated with the friction coefficient or model selected for the FE analysis in this study. For example, [PERSON] et al. [24] revealed that the friction conditions along both the rolling and width directions of a workpiece significantly affected the strip shape and profile during the strip flat rolling process. In this study, a constant friction coefficient of 0.15 was used from the previous study [22] and the experiences of the authors. Based on the comparison of the shape change of the flattened  
Figure 3: Comparison of (**a**) effective strain contours, (**b**) \(W\) and \(b\), and (**c**) \(\varepsilon_{\text{max}}\) of the wire with the number of elements at a roll diameter of 400 mm.  
wire with \(R_{\text{h}}\), a higher friction coefficient was necessary to more accurately predict the shape change of the flat-rolled wire because it is known that the lateral spreading of the flat-rolled wire increases with the friction coefficient [25]. In addition, roll flattening also affected the wire shape because the elastic deformation of rolls becomes larger and more important as the strength of the workpiece increases [26]. That is, the effective roll diameter increases during the cold rolling process owing to the roll flattening. In this FE analysis, the roll was considered to be a rigid body, which can lead to a decrease in the \(b\) and \(W\) values of the flat-rolled wire during FE analysis.  
Overall, although minor errors existed between the results of the experiment and the FE analysis, it can be concluded that the FE model used in this study was adequate for analyzing the flat-rolled wire during the process because the errors of the \(W\) and \(b\) values between the experiments and simulations were relatively small, namely, within 5%.
# Low-Complexity Channel Estimation in 5G Massive MIMO-OFDM Systems
## 1 Introduction
The use of spatial common sparsity can not only improve the estimation performance, but also save the pilot overhead, and enable the algorithm to obtain the sparsity adaptive ability. The main contributions of this paper are:  
1. It improves the existing CoSaMP [14] algorithm from the dynamic sparsity adaptive and structural aspects. 2. The proposed algorithm has a feature to estimate the channel without unknown sparsity. This is realized by the threshold parameter \(\beta\) which provides information on when to stop the iterations to get the required sparsity of channel. 3. Based on the adaptive sparsity, the massive MIMO channel space-time common sparse characteristics are used for structural processing. This not only makes the acquisition of sparsity faster but also improves estimation performance and accuracy. 4. A simulation program verifies the accuracy and performance comparison of the proposed channel estimation algorithm with the existing CoSaMP [14], structured CoSaMP (S-CoSaMP) [9], and structured turbo-CS [14] algorithms. Furthermore, the proposed method utilizes the unique feature of threshold \(\beta\)-based iteration control, which in turn depends on the SNR level. This approach enables us to determine the unknown sparsity in an indirect manner.
## 4 Experiments  
### Datasets and Settings  
The original data used in this research are the collected high-resolution Google images, which have the same resolution and are distributed in different regions. First, the open-pit mining areas on the images are manually labeled according to the pre-determined decision rules. Second, the original image data and the labeled data are sliced according to the storage format and size requirements of the model input data to form the original and labeled image database in PNG format with a size of 512 \(\times\) 512. In this case, the original image slice picture still contains three bands, while the labeled picture has only one band. The label value of each pixel is represented by a number, using the UINT8 data type for storage, and the value indicates whether it is a mining area or not: \"1\" represents that it is a mining area, and \"0\" indicates a non-mining area. The dataset has 3437 image pairs, which are randomly divided into 2946 training images and 491 test images according to the ratio 6:1, and there are two classes: open-pit mining area class and background class.  
The computer used for the experiments was configured with an NVIDIA GeForce RTX2080 GPU (NVIDIA, Santa Clara, CA, USA) graphics card, and the Ubuntu operating system, Python 3.6, CUDA 11.4, and PaddlePaddle 2.4.2 deep learning framework were installed. In the subsequent experiments, all seven models were implemented and run based on PaddlePaddle 2.4.2.
## 2 Experimental Procedures
### Test Procedure
#### 2.3.3 Compression Strength  
The compression test was done to determine the compressive strength in the plane of the panel (Figure 5). Compression was measured according to ASTM D1037-06 Section 12 [20]. The specimens' dimensions were \(102\times 25\times 20\) mm (rectangular pieces). Three samples were used for the test as replicates. The specimen was centred carefully through a spherical loading block in the testing machine in a vertical plane for unsupported (102 mm) specimen. The specimen was loaded parallel to the panel face until failure occurred. Other than what was suggested in ASTM D1037, the load speed was constant at 0.5 mm/min instead of 0.005 mm/min. The maximum load realized during the test before specimen failure occurred was recorded and used to calculate the compression strength, as shown in (5).  
\[\mathrm{CS}=\frac{\mathrm{P}_{\mathrm{max}}}{\mathrm{bd}} \tag{5}\]  
where:  
CS: Compressive strength, MPa.
# Internet of Things and Blockchain Integration: Security, Privacy, Technical, and Design Challenges
## 4 RQ 3--BIoT Integration: Future Concerns and Trends
### IoT, Blockchain, and Edge Computing Integration  
Edge computing and software-defined networking (SDN) are two solutions offered in the recent literature to reduce IoT difficulties related to capacity and capabilities [12; 142]. The concept is to transfer BC processes and consensus to the edge or SDN, which has additional capabilities and storage space. This appears to be a sensible problem-solving approach [102]. In the edge and SDN architecture, the BC system's nodes are all linked to the network, and each one keeps a copy of the transaction [105]. These data are often updated across all nodes to maintain their integrity. This enables a large number of nodes to make verification decisions based on prior transactions even while they do not trust one another. This improves security and privacy, as well as eliminates the need for centralized authentication authorities. In the SDN architecture, in order to provide an appropriate attack detection model, SDN continuously monitors and analyzes traffic data throughout the whole IoT network, whereas BC offers distributed detection mechanisms to address the single point of failure problem with the current design [142; 150]. However, the scalability, lack of standards, and interoperability issues that these technologies (i.e., fog computing and edge computing) also compromise should be taken into account [162].
# Quantum Key Distribution in the Presence of the Intercept-Resend with Faked States Attack
## 5 Discussion
### Measurement Device-Independent QKD  
In [17], a \(QKD\) system that is intended to be an independent measurement device has been introduced. Implementations of this approach have been demonstrated in [24; 25]. However, [24] requires several additional optical components different from the \(BB84\) protocol, and [25] relies on decoy states. Since \(QKD\) must be as device independent as possible, adding hardware to the system may aggravate it. In contrast, the _nack state_ protocol relies on the hardware of the original \(BB84\) protocol, which has been extensively studied.  
Other protocols have been designed to resist the \(IRFS\) attack: varying randomly the efficiency of the detectors [26], monitoring a rate of coincidence detection at a pair of single photon detectors [27] or simply adding watchdogs detectors [28]. However, the _nack state_ protocol exhibits two main advantages:  
1. The protocol uses the same optical equipment as the \(BB84\). It does not use any other extra hardware;
2. The protocol or its dual protocol, the _ack \(QKD\)_, could be used to detect other attacks, such as the Photon Number Splitting attack (_PNS_) [19].  
Thus, the _nack state_ protocol would be useful to design a more secure and efficient \(QKD\) protocol.
# Adaptive Pitch Control of Variable-Pitch PMSG Based Wind Turbine
## 4 Simulation Results  
To verify the effectiveness of the proposed NAC, simulations studies (Matlab/Simulink) have been carried out by comparing with the VC, GSPI and FLC. In this paper, a 2 MW PMSG-WT given in [35] is investigated. The parameters of the PMSG-WT system are listed in Table 1. In this paper, the mechanical rotation speed reference is \(\omega_{\rm mr}=2.2489\) rad/s. The reference of d-axis stator current is \(i_{\rm mdr}=0\) A. The rated electromagnetic torque reference is \(T_{\rm er}=889326.7\) Nm. According to Equation (7), the q-axis stator current reference is \(i_{\rm mqr}=593.3789\) A.  
Parameters of NACs for subsystems \(S_{1}\), \(S_{2}\), and \(S_{3}\) are designed based on pole-placement and listed in Table 2. Please note that the controller parameters of the FLC are the same as that of the NAC for all three subsystems, and the FLC requires exact system parameters and full state measurements except \(\frac{\mathrm{d}V}{\mathrm{d}t}\).  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameters** & **Values** & **Units** \\ \hline Air density \(\rho\) & 1.205 & kg/m\({}^{3}\) \\ Rated wind speed \(V_{\rm r}\) & 12 & m/s \\ Blade radius \(R\) & 39 & m \\ Actuator time constant \(\tau_{\beta}\) & 1 & s \\ pitch angle rate \(\beta_{\rm rate}\) & \(\pm 10\) & degree/s \\ Rated output power \(P_{\rm r}\) & 2 & MW \\ Stator resistance \(R_{\rm s}\) & 50 & \(\upmu\Omega\) \\ \(d\)-axis inductance \(L_{\rm d}\) & 5.5 & mH \\ \(q\)-axis inductance \(L_{\rm q}\) & 3.75 & mH \\ Number of pole pairs \(p\) & 11 & \\ Field flux \(K_{\rm e}\) & 136.25 & \(\rm V\cdot s/rad\) \\ Total inertia \(\rm J_{\rm tot}\) & 10,000 & kg \(\cdot\) m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of PMSG-WT for simulation studies.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{2}{c}{**Parameters of the NAC Equation (42)**} \\ \hline Gains of observer Equation (19) & \(\alpha_{11}=50\), \(\alpha_{12}=1.875\times 10^{3}\), \(\alpha_{13}=1.5625\times 10^{4}\), \(\epsilon_{1}=0.02\) \\ Gains of observer Equation (38) & \(\alpha_{21}=4\times 10^{2}\), \(\alpha_{22}=4\times 10^{4}\), \(\epsilon_{2}=0.01\) \\ Gains of observer Equation (39) & \(\alpha_{31}=4\times 10^{2}\), \(\alpha_{32}=4\times 10^{4}\), \(\epsilon_{3}=0.01\) \\ Gains of linear controller Equation (41) & \(k_{11}=40\), \(k_{12}=4\times 10^{2}\), \(k_{21}=1.6\times 10^{2}\), \(k_{31}=1.6\times 10^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of Pitch control approach for simulation studies.