esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Photovoltaic Properties
#### 3.3.4 Open-Circuit Voltage (\(V_{\text{OC}}\))  
The open-circuit voltage (\(V_{\text{OC}}\)) in a DSSC represents the maximum voltage generated by the cell when there is no external load or current flowing through the cell, reflecting the voltage across the DSSC terminals in the absence of electrical current. \(V_{OC}\) is closely tied to the energy gap between the redox potential of the electrolyte and the conduction band of the semiconductor, playing a crucial role in DSSC performance. As the energy difference widens, the \(V_{OC}\) of the DSSC increases. \(V_{OC}\) is also linked to the cell's maximum theoretical efficiency, as determined by the Shockley-Queisser limit [44].  
Several factors, including the choice of dye and semiconductor, the thickness and quality of the semiconductor layer, and the composition and redox potential of the electrolyte, can affect the \(V_{OC}\) in a DSSC. The power conversion efficiency in DSSCs can be calculated by using the \(V_{OC}\). Typically, the dye molecules transfer electrons from their LUMO to the semiconductor conduction band. This also means that if the \(E_{LUMO}\) is high, the \(V_{OC}\) will also be high. The following Equation (5) has been used to compute the theoretical values of open-circuit voltage (\(V_{OC}\)).  
\[V_{OC}=E_{LUMO}-E_{CB} \tag{5}\]  
where:  
\(V_{OC}=\) Open-circuit voltage  
\(E_{LUMO}=\) the energy of the LUMO  
\(E_{CB}=\) the energy of the semiconductor's conduction band.  
Table 3 shows the \(V_{OC}\) values for natural dyes in both the gas and solvent phases. In the solvent phase, the order of the \(V_{OC}\) values is delphinidin > petunidin > cyanidin > peonidin > pelargonidin, while for the gas phase is petunidin > delphinidin > peonidin > pelargonidin. In both the gas and solvent phase, petunidin and delphinidin exhibited the highest \(V_{OC}\) levels among all the dyes. This suggests that petunidin and delphinidin have a better chance than the other dyes of generating greater voltage outputs in DSSCs. Due to their higher \(V_{OC}\) values, petunidin and delphinidin are recommended as viable choices for DSSC applications. By permitting the formation of greater electrical potentials, which is necessary for effective power conversion, these dyes can help increase device performance.
# Impacts of Integration of Wind Farms on Power System Transient Stability
## 4 Case Studies, Results and Discussion
### Impact of Varied Wind Speed on System Transient Stability
#### 4.3.1 For Wind Speed at 20 m/s  
The wind speed of wind turbine is set to 20 m/s to see the possible effect on power system transient stability. As demonstrated in Figure 11, the speed magnitudes of all generators fluctuate with the peak value 1.008 p.u. and settle down in 9 s. Now, if we look at the generator angle response in Figure 12, the first positive and negative peaks are at different levels for different generators, and all of them regain synchronism after 9 s.
# An Unsupervised Machine Learning Approach for UAV-Aided Offloading of 5G Cellular Networks
## 1 Introduction
### Contributions  
In this paper, we consider a two-tier 5G heterogeneous cloud radio access network (HCRAN) [18], which is configured to cope with events of exponential increase in data traffic. The first tier consists of macrocells providing coverage to the whole region of interest, while UAV-BSs form the second tier to maximize macrocells' traffic offloading. The primary objective of this work is to identify the minimum number of UAV-BSs that should be deployed, as well as their best placement within the coverage area of the macrocells, aiming to maximize traffic offloading. Towards this end, we partition the users in the affected area into clusters utilizing the k-medoid algorithm [19]. Then, we identify the number of UAV-BSs that should be deployed, considering both the number of the formed clusters and the maximum number of users that a UAV-BS can serve. Given that the number of the available UAV-BSs may be less than the minimum UAV-BSs required to cover the formed clusters, we also propose a cluster selection process scheme. The proposed cluster selection technique is formulated as an optimization problem, which its objective function is to maximize the traffic offload subject to the constraint that the deployed number of UAV-BSs should be less or equal to the available one. The proposed cluster selection scheme will provide the clusters that the available UAV-BSs should be deployed to achieve the maximum traffic offload. Furthermore, computer simulations are carried out to evaluate the performance of the proposed scheme in terms of percentage offloading, average received signal strength, throughput, spectral efficiency, sum rate and the effectiveness of localization inaccuracy. Further, we compare the proposed system with a two-tier 5G H-CRAN consisting of either random or planned picocells and other unsupervised machine-learning-based methods for UAV-BS best placement. More specifically, the following contributions are provided:  
* A novel UAV-aided offloading framework is proposed, in the context of an overloaded terrestrial 5G network. The offloading procedure can be regarded as a cluster formulation problem that can be dealt with unsupervised machine learning-based methods. To the best of the authors' knowledge, this is the first time that the k-medoid algorithm is utilized in this context.
* A clustering selection scheme is proposed, which is formulated as an optimization problem to further enhance the offloading procedure under a limited number of available UAV-BSs.
* The proposed scheme mainly increases the offloading percentage as well as the spectral efficiency and, at the same time, improves the received signal strength, as compared to random or planned picocells deployment strategies and the k-means algorithm for the cluster formulation problem.
* The impact of the increased localization inaccuracy of the UEs on the proposed framework is evaluated. Also, comparisons concerning the performance of the proposed method with another state-of-the-art unsupervised machine learning method are illustrated and discussed.
# Rethinking the Operation Pattern for Anomaly Detection in Industrial Cyber-Physical Systems
## 5 The Framework of Proposed Unsupervised Anomaly Detection Model
### The First Defence Line  
This defence line can detect several elementary attacks that violate the Modbus TCP protocol in the data preprocessing phase. These attacks are summarised as follows:  
* **Malformed packets.** Packets do not obey the standard protocol implementations, such as the length value not being equal to the actual payload size. These packets are usually observed in fuzzing or manipulating attacks.
* **Missing responses.** Master devices do not receive the expected responses. These anomalies often occur when attackers use selective forwarding to block specific responses.
* **Duplicated responses.** For a request, the master devices receive two or more response messages. These anomalies usually occur when attackers try to disturb the production processes by injecting false responses or invalid control commands [39].
* **Unmatched responses.** The observed responses have no corresponding requests. Many attacks, such as tampering and replay attacks, could trigger this malicious behaviour.  
To detect these violations, the model first parses the raw industrial traffic into plain text format according to Modbus TCP protocol. If the parse fails, the model reports these packets as malformed. Then, the model matches the request and response messages according to \(IP_{mst}\), \(Add_{stlv}\), \(FN\) and transaction identifier to examine if the request and response messages are one-to-one mapped. If they are not one-to-one mapped, this defence line reports this abnormality, along with corresponding causes.  
Once this defence line identifies packets as malicious, the model directly reports them along with the above-mentioned causes and stops further examinations.
###### Abstract
To solve these challenges, a novel framework for the simultaneous tracking and recognition of drone targets using mmWave radar is proposed. The proposed framework is based on the installation of a low-cost and small-sized mmWave sensor for transmitting and receiving signals, as shown in Figure 1B. Our main objective was to utilize 3D point clouds generated by a mmWave radar to detect, track, and recognize multiple moving  
Figure 1: In contrast to (**A**), an optic-based system, (**B**) the proposed framework is based on mmWave radar, which consists of three transmitting antennas and four receiving antennas. targets, including drone and non-drone targets. When raw analog-to-digital conversion data from antenna arrays are converted into 3D point clouds, the data size is reduced from tens of gigabytes to a few megabytes [23]. This allows for the faster data transfer, processing, and application of complex machine learning algorithms. Unlike the micro-Doppler signature, the spatiotemporal features of cloud points are more representative and easily interpretable because movements occur in a 3D space. For performance evaluation, a dataset ([[https://github.com/00](https://github.com/00) Nave198/MSU-ECE480-13-mmWave/tree/main/data]([https://github.com/00](https://github.com/00) Nave198/MSU-ECE480-13-mmWave/tree/main/data), accessed on 1 August 2023) collected with an IWR6843 ISK mmWave sensor by Texas Instruments (TI) was used for training and testing the CNN. Our key contributions can be summarized as follows:  
1. A signal-processing algorithm is proposed to generate 3D point clouds of multiple moving targets in the field of view (FoV), considering both static and dynamic reflections of mmWave radar signals. 2. A multitarget tracking algorithm was developed that integrates a density-based clustering algorithm to merge related point clouds into clusters, extended Kalman filters (EKF) to estimate the new position of the detected targets, and the Hungarian algorithm to match each new estimated track with its target cluster. 3. A novel CNN model is proposed for feature extraction and identification of drone and non-drone targets from clustered 3D cloud points. 4. The performance of the proposed tracking and recognition algorithms was evaluated. The rest of this paper is organized as follows. Section 2 reviews the existing literature. In Section 3, the proposed framework is presented. Section 4 describes the signal preprocessing. The clustering process is described in Section 5. The tracking process is described in Section 6. Section 7 describes the recognition process. Section 8 discusses the performance evaluation. Section 9 presents conclusions and suggestions for future work.
## 3 Results
### Algorithm Performance
Note that while the AIS Gbest and Hybrid algorithms reached minima significantly faster, the minima they converge to were larger than those reached by AIS Lbest or DH-BPSO. In terms of real time to converge to a minimum cost, AIS Gbest and Hybrid algorithms exceed. However, this convergence speed comes at the cost of a larger minimum cost achieved. Additionally, several factors influence the real time required to achieve a minimum cost. These include the method by which the algorithm is implemented in code and other processes executing on the computer during benchmarking. Hence, average iterations to converge to a minimum is a more useful metric. This and the average minimum cost achieved are the primary metrics used to select an algorithm for antenna design applications. When applying PSO algorithms to antenna design, the cost function is usually some electrical characteristic of an antenna determined via a full-wave simulation. In such cases, each particle represents an antenna configuration that must be simulated to determine the cost. If there are several design variables (i.e., large \(N\)), the optimization may run for an unacceptably long time or consume an excessive amount of computational resources. Hence, when selecting an algorithm for antenna design, the statistic of interest is the average minimum cost achieved and the average iterations required to reach a minimum cost. Additionally, the changes in electrical characteristics induced by a change in antenna structure are often unpredictable. It is likely multiple satisfactory solutions exist. That is, the solution space of such a problem may have multiple, distinctly large minima, and only one of which may be the true global minima. The shape of the Eggholder function emulates this. An algorithm that explores a wide range of solutions but is resilient to premature convergence to local minima is ideal. Given these criteria, the DH-BPSO algorithm promises the best performance. It exceled in optimizing the Eggholder function in both average minimum cost achieved and average iterations required to reach a minimum cost. Thus, the DH-BPSO algorithm appears a promising tool in the design and optimization of antennas. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Function** & **Average Minimum Cost** & **Average Iterations to Minimum** & **Average Time to Minimum (ms)** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Column plots comparing AIS BPSO algorithms solving 100-variable test functions using 10 particles and a maximum iteration of 200 averaged over 500 runs.
## 4 Results and Discussions  
### Validation of the Mechanical System  
To validate the system, we compared the resonance frequencies obtained from analytical and experimental models. The analytical model utilized the transfer function of both piezoelectric transducers, while the experimental model involved an impact test using a hammer and a frequency response captured by an accelerometer. A frequency ramp (0 to 50 Hz) further confirmed resonance by measuring the maximum voltage on an oscilloscope. As illustrated in Figure 5(b) and Table 3, the peaks in oscillation amplitude represent the natural resonance frequencies, where the system achieves maximum energy and efficiency.  
These experimental methods allow for a precise determination of the resonance region, critical for optimizing energy capture from the transducers.  
It is noted that the PZT-5H piezoelectric exhibited a larger amplitude due to its larger dimensions and lower rigidity, allowing it to generate higher energy levels at its resonance frequency. The peaks determine the region of resonance and capture better energy levels, and the results are acceptable. After the experiments, errors of 1.63% and 4.50% were obtained with the analyzer, and 2.73% and 2.57% with the oscilloscope, for the PZT-5H and P5-13B transducers, respectively.
## 4 Experiments and Analysis
### Deviation Assessment of the Method  
In order to verify the reliability of the measurement results, the verification measurement of the test profiles is carried out on a high-precision four-coordinate measuring machine of JE42, whose overall measurement accuracy is less than 2 \(\upmu\)m. The four-coordinate measuring machine of JE42 collects coordinate data with a high-precision contact probe of GT31 from TESA Switzerland, and its technical parameters are provided in Table 3. After coordinate transformation and registration of the measured data, the measurement curves of test profiles are shown in Figure 10.  
Figure 10: Measurement curves of test profiles on JE42.  
The experimental data collected by JE42 is processed based on least squares fitting algorithm, and the form and position parameters of the leading and trailing edges for test profiles I and II are solved as shown in Table 4.  
Based on the parameters solved of the leading and trailing edges and evaluation model of blade twist proposed in the study, the results are as follows:  
\[\left\{\begin{array}{ll}\overline{\psi}_{0}=-14.356^{\circ}\\ \eta_{0}=14.776^{\circ}\end{array}\right. \tag{17}\]  
where \(\overline{\psi}_{0}\) and \(\eta_{0}\) represents respectively the twist angular position and twist angle measured by JE42.  
Taking the measurement results of JE42 as the agreed true value, the measurement deviation of twist angular position is less than 0.02\({}^{\circ}\), and the measurement deviation of twist angle is less than 0.04\({}^{\circ}\). The experiments show that the method proposed in this study can achieve high-precision and form-free measurement of blade twist.
## 2 Study Area and Data
### Sample Data
#### 2.2.1 Soil Samples  
In Xiaoyan Town, Nanzhang County, surface soil samples were collected using a combination of grid and polygonal sampling based on a predetermined sampling map. A handheld GPS device was used to pinpoint sampling locations, with an average sampling density of 4.10 points/km\({}^{2}\). Surface soil samples and duplicate samples were collected from the 0 to 20 cm soil layer, removing debris such as weeds, roots, stones, bricks, and fertilizer clumps. To enhance representativeness, a primary sampling point was selected as the fixed location, and 3-5 subsamples were collected within a 50 m radius and combined into a single sample.  
Soil sample processing included drying, sieving, packing, completing sample submission forms, assigning sample IDs, and submitting samples for analysis. Surface soil samples were air-dried, sieved through a 20-mesh sieve, and sent to the laboratory for analysis. In total, 29 elements were analyzed, including Se, Cd, Hg, As, Pb, Cu, Zn, Cr, Ni, Mo, Mn, B, N, Co, I, F, Sr, Ge, P, Corg, S, Cl, Fe, Na, K, Ca, Mg, Si, and Al. Besides, pH was measured as well.
## 2 Introduction to IPv6
### Transition Methods
#### 2.3.1 Coexistence of IPv4 and IPv6  
Dual stacking is probably the easiest way to establish a transition without too many outages. In some cases, however, this might not be possible. Incompatible hardware may be too expensive to replace, so some parts of the network have to stay with IPv4 (at least for a while). Furthermore, dual stacking adds complexity and increases administration workload. This situation leads to a need for further transition or translation mechanics.  
6 to4 and Teredo--described in Section 2.3.3.--are mechanics used for tunneling. IPv4-compatible addresses are deprecated because none of the specified transition methods use it anymore. The use of IPv4-mapped IPv6 addresses is discussed in RFC 4038 [44]. The address structure is _::FFFF/96_ + IPv4 address (e.g., _::FFFF:123.45.67.89_). Basically, mapped addresses are used in dual stack networks that are still in transition where a IPv4 only node would like to access an IPv6-only application. However, the use of IPv4-mapped addresses is disabled by default in many systems because of security concerns.  
Possibilities to translate IPv4 to IPv6 were enhanced in RFC 6052. The structure is shown in Figure 3. While formerly/96 prefixes were used like ::/96 or ::FFFF/96, the new standard allows network specific prefixes in various lengths. The length depends on the allocated network prefix. If a unique/96 prefix is used, the IPv4 address does not have to be globally unique. The resulting IPv6 address must be unique if it is supposed to be a global unicast address. If the network does not have its own network prefix, a special prefix can be used. This prefix is called Well-Known prefix and has the form _64:ff9b:/96_.  
Figure 2: Transition methods.  
Figure 3 shows six specified possibilities to create an IPv4-embedded IPv6 address. Depending on the length of the prefix (green) the structure changes. If the prefix is 96 bit long, the IPv4 address (purple) is just added after the prefix to reach a length of 128 bit. If the prefix is shorter than 96 bit, the IPv4 address is interrupted by an octet of zeros (red). This octet is always placed at the positions 64 through 71. The rest of the address is reserved for a suffix (orange) which is not used yet and has to be set to all zeros. This suffix could be used in the future for additional functions, so IPv4-embedded IPv6 addresses might have to be changed. Many address blocks of the IPv6 address space are still to be assigned to a particular purpose. Overall this accounts for 86% of the available address space. This space is reserved for future developments, which is why IPv6 is--and will continue to be--very flexible for a long time.
## 1 Introduction
### Background
The patient's vital signs may include heart beats, temperature, blood pressure, motion/acceleration, pulse-oximetry _etc._ Thus patients could benefit for continuous long-term monitoring after returning home from the hospital. \begin{table}
\begin{tabular}{l l} \hline \hline
**Generic WSNs** & **WMSNs** \\ \hline Automatic and standalone & Human involvement \\ Scalability (_i.e._, large scale) & Scalability (_i.e._, small scale) \\ Fixed or distributed deployment & Mobility \\ Reliability (data rate depend on applications) & Reliability (high data rate) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Difference between generic WSN and WMSN. Figure 1: Interdisciplinary research of WMSN. As shown in Figure 2, WMSNs carry the promise of quality-of-care across wide variety of healthcare applications (e.g., ambulatory monitoring, vital sign monitoring in-hospitals, elderly peoples' at home care monitoring, monitoring in mass-casualty disasters, clinical monitoring, _etc._). In addition, other applications that also benefit from WMSNs include sports-person health status monitoring [10], and patients' self-care (_i.e._, a BAN network on a diabetic patient could be helpful to auto inject insulin though a pump, as soon as their insulin level declines). So far several research groups and projects have started to develop health monitoring using wireless sensor networks, for example, CodeBlue [7], LiveNet [11], MobiHealth [12], Alarm-Net [13], UbiMon [14], ReMoteCare [15], MobiCare [16,17], Lifeguard [18], AID-N [19], CareNet [20], ASNET [21], WiMoCa [22], SAPHIRE [23], THE-MUSS [24]. Thus, healthcare systems are the most beneficial applications using wireless medical sensor technology that can perform patient care within homes, hospitals, clinics, disaster sites and the open environment.