esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
### Computation Offloading Model  
The UE generates a series of data processing tasks that need to be executed in a timely manner for the low latency requirement. Due to the constrained energy and computation resources of UE, the task can be offloaded to the BS. The computation task generated by UE \(k(k\in\mathcal{K})\) at time slot \(t\) is denoted by a tuple \(D_{k}(t)=\left\{d_{k}(t),c_{k}(t),\xi_{k}(t)\right\}\), where \(d_{k}(t)\) denotes the input data size (bits), \(c_{k}(t)\) represents the required computation cost (e.g., the number of CPU cycles for processing one-bit data), and \(\xi_{k}(t)\) indicates the maximum tolerable latency of UE \(k\), respectively. We assume that tasks are bitwise separable and can be partially executed locally, while the remaining parts directly send the raw data to the BS for processing. The processing delay of the BS server executing task \(D_{k}(t)\) can be calculated by  
\[T_{o,k}=\frac{d_{k}(t)c_{k}(t)}{f_{o,k}(t)}, \tag{13}\]  
where the \(f_{o,k}(t)\) represents the CPU frequency of the BS server. The processing delay of UE \(k\) to execute the task \(D_{k}(t)\) locally can be written as  
\[T_{l,k}=\frac{d_{k}(t)c_{k}(t)}{f_{l,k}(t)}, \tag{14}\]  
where \(f_{l,k}\) is the CPU frequency of UE \(k\) (cycles/s). The overall latency for processing the task \(D_{k}(t)\) is depicted as  
\[T_{k}^{\text{tol}}=w_{k}(T_{o,k}+T_{u,k}+T_{d,k})+(1-w_{k})T_{l,k}, \tag{15}\]  
where \(w_{k}\in[0,1]\) represents the offloading ratio. Under two extremes, \(w_{k}=1\) when the task is offloaded to BS and \(w_{k}=0\) when the task is processed locally at the UE \(k\). \(T_{u,k}=d_{k}(t)/r_{k}(t)\) is the uplink transmission delay with the uplink data rate \(r_{k}\), which is listed as  
\[r_{k}(t)=B_{k}\log_{2}\left(1+\frac{p_{k}\left|\mathbf{h}_{m,1}^{T}\mathbf{ \Phi}\mathbf{h}_{k,2}\right|^{2}}{\sum_{i=1,i\
eq k}^{K}p_{i}\left|\mathbf{h}_ {m,1}^{T}\mathbf{\Phi}\mathbf{h}_{i,2}\right|^{2}+\sigma_{c}^{2}}\right), \tag{16}\]  
where \(B_{k}\) and \(p_{k}\) are the uplink transmit bandwidth and power for UE \(k\), respectively. Due to the small size of the processing result, the latency of receiving the result \(T_{d,k}\) can be ignored [34; 35].  
Meanwhile, the energy consumption of executing task offloading by the UE \(k\) can be denoted by  
\[E_{o,k}=\kappa_{o}f_{o,k}^{2}(t)d_{k}(t)c_{k}(t), \tag{17}\]where \(\kappa_{o}\) denotes the effective capacitance coefficients related to the chip architecture [36; 37]. The energy consumption for UE \(k\) executing the task locally can be formulated as  
\[E_{l,k}=\kappa_{l}f_{l,k}^{2}(t)d_{k}(t)c_{k}(t). \tag{18}\]  
Similarly, \(\kappa_{l}\) is the effective capacitance coefficient. Therefore, the overall energy consumption can be given by  
\[E_{k}^{\text{tol}}=w_{k}(E_{o,k}+E_{u,k}+E_{d,k})+(1-w_{k})E_{l,k}, \tag{19}\]  
where \(E_{u,k}\) represents the offloading energy consumption with \(E_{u,k}=p_{k}d_{k}/r_{k}\). The energy consumption for result receiving can also be ignored.
# Mind Mapping Training's Effects on Divergent Thinking Skills: Detection Based on EEG Data
## 3 Materials and Methods
### Experimental Procedure
#### 3.4.1 EEG Data Acquisition  
EEG signals were filtered between 0.1 Hz and 100 Hz, and a 50 Hz notch filter was applied to avoid power line interference. Electrode impedances were maintained below 5 k\(\Omega\). All signals were sampled at a frequency of 512 Hz. The EMOTIV EPOC X was visually inspected to ensure it was free of eye-movement artifacts and contained no electromyographic activity. Data processing was based on artifact-free EEG recordings with Hanning windowed epochs. For each participant and each experimental condition, the filtered and artifact-free EEG epochs were selected. Figure 3 illustrates the schematic diagram of the EEG data acquisition setup. The participant sits on a chair facing the PC, wearing the EMOTIV EPOC X, and performs the translation tasks while EEG data are recorded. The EMOTIV EPOC X transmits data to the PC via Bluetooth, which is accessed through the EmotivPRO 3.6.9 integrated software on the computer. Data packets are sent every second, and by analyzing these packets, numerical data for the alpha band can be extracted. The averaged alpha band power is then calculated using Fast Fourier Transformation (FFT).
## 3 Results and Discussion
### Removal Capability of FeBC in Actual Industrial Wastewater  
In order to study the Cr(VI) removal performance of FeBC in actual wastewater, FeBC was used to adsorb electroplating wastewater at a dosage of 10 g\(\cdot\)L\({}^{-1}\); the results are shown in Table 4. Before treatment, the total Cr and Cr(VI) concentration in the wastewater exceeded the integrated wastewater discharge standard of China (GB 8978-1996) [39]. However, after adsorption, it was found that the removal efficiency of the total Cr and Cr(VI) by FeBC reached 93.36% and 96.77%, respectively. Both the concentration of total Cr and Cr(VI) in the wastewater were in compliance with the standard. After the adsorption treatment, the wastewater was drained into the city sewage pipe and continued to be treated. In addition, modified biochar also have the potential to remove chromium (Cr) from different actual wastewater. [PERSON] et al. [14] investigated the feasibility of FSBC to adsorb Cr in actual tanning wastewater. After adsorbing, the total Cr and Cr(VI) removal efficiency reached 98.50% and 99.60%, respectively, and the concentration of total Cr and Cr(VI) all were in standard.  
Some studies indicate that alkali solution or Na\({}_{2}\)EDTA solution can desorb Cr from FeBC [40; 41; 41], which may help to realize the recycling and reuse of the adsorbent. The Cr (VI) removal rate decreases as the adsorption cycles increase. Finally, the inefficient adsorbent with toxic Cr is included in hazardous waste management and collected for incineration treatment. After incineration, ash containing chromium is collected for recycling.
## 4 Discussion
### Accuracy Assessment  
Cohen's Kappa statistic is widely employed by the remote sensing community as a standard measure for accuracy assessment of the classified images, although we acknowledge criticisms of this technique [65; 66]. It was evident from the error matrix approach that high burn fire risk areas can be detected most accurately compared to low burn risk areas owing to the burnt forest canopy and unobstructed view of the ground area. Using dNBR and RdNBR in conjunction with MTMF to derive fire risk zones improved overall accuracy compared to using only multispectral data for traditional fire assessment similar to studies done by [PERSON] et al. [67] and [PERSON] et al. [13]. Previous studies concerned either with pixel-based or polygon based procedures for accuracy assessment reported 85% overall accuracy. In our pixel-based approach, we obtained 89.4% overall accuracy, which is considered as a moderate accuracy for classification [68; 69]. However, the Cohen' Kappa statistics have limitations on accuracy measures due to randomness, quantity disagreement, and allocation disagreement, and is redundant with overall accuracy [65; 68].
# Investigation of Electro-Elastic Properties for LN Single Crystals at Low Temperature
## 3 Results and Discussion
### Temperature Dependence of Electromechanical Coupling Factor and Piezolectric Coefficient  
The electromechanical coupling factors for different vibration modes, including the transverse length extensional mode, the longitudinal length extensional vibration mode and the thickness shear vibration mode of LN crystals, were investigated over a temperature range from \(-150\)\({}^{\circ}\)C to 150 \({}^{\circ}\)C. The result was presented in Figure 4. It was observed that the LN crystals possessed high electromechanical coupling factors, \(k_{15}\), measuring approximately 61.1-61.9% over the tested temperature range. The longitudinal length extensional vibration modes, \(k_{22}\) and \(k_{33}\), showed different trends: \(k_{22}\) was determined to be 29.7% at \(-150\)\({}^{\circ}\)C, then slightly declined to 29.4% at 150 \({}^{\circ}\)C; while \(k_{33}\) presented an increase from 22.1% to 23.4%, with a variation of 5.6%. The transverse length extensional mode \(k_{31}\) was relatively small; approximately 2.5% at room temperature.  
Combined with the determined dielectric constants, elastic compliances and electromechanical coupling factors, the variations in piezoelectric coefficients were obtained, as presented in Figure 5. The \(d_{15}\) exhibited the largest piezoelectric response and a stable trend from \(-150\)\({}^{\circ}\)C to 150 \({}^{\circ}\)C; the variation was lower than 10%. The larger piezoelectric coefficient was beneficial to enhance the sensitivity of the transducers. The \(d_{22}\), \(d_{33}\) and \(d_{31}\) factors demonstrated growth as the temperature increased; the variations were 7.5%, 25.6% and 41.4%, respectively. The larger variations of \(d_{33}\) and \(d_{31}\) were attributed to the larger increase in the relative dielectric permittivity \(\varepsilon_{33}^{\mathsf{T}}/\varepsilon_{0}\). Furthermore, the variation of the piezoelectric coefficient \(d_{22}\) for LN and \(\alpha\)-BIBO crystals was investigated. The results were presented in the small inset of Figure 5. Compared with the \(\alpha\)-BIBO crystal, LN showed smaller variations in the same temperature range. The large piezoelectric coefficients and a  
Figure 3: Variation of elastic compliance \(s_{ij}\) for LN crystals.  
good temperature stability were beneficial for the transducer applications of LN crystals at low temperatures.
# Regulated Power Supply with High Power Factor for Hyperspectral Imaging Applications
## 2 Background Concepts  
### Total Harmonic Distortion  
The term total harmonic distortion (THD) is a way to define the distortion factor of a signal. This concept refers to the measurement of the quality of a waveform and is defined as the relationship between the harmonic content of the signal (\(I_{n,RMS~{}IN}\)) and the first harmonic (\(I_{1,RMS~{}IN}\)), as it is presented in (1). From the definition of instantaneous current (2), the effective or root mean squared (RMS) current can be defined by (3). For the nth harmonic, the effective value of the current can be calculated using (4). The THD value is a percentage that lies between zero and infinity and, ideally, is as low as possible (close to zero).  
\[THD=\frac{\sqrt{\sum_{n=1}^{\infty}{I_{n,RMS~{}IN}}^{2}}}{I_{1,RMS~{}IN}}\times 1 00\% \tag{1}\]  
\[i(t)=I\cdot\!sen(\omega t) \tag{2}\]  
\[I_{rms}=\sqrt{\frac{1}{T}\!\int_{0}^{T}{i^{2}(t).dt}} \tag{3}\]  
\[I_{n,rms}=\left(\frac{I_{n,max}}{\sqrt{2}}\right) \tag{4}\]  
The harmonic distortion of a signal can be caused by a transient phenomenon, such as the starting of a motor or short circuit failures, or by permanent conditions mainly related to steady-state harmonics. For the latter, standards are established to define the permissible limits of distortion. The presence of undesired harmonics can negatively affect the spectrum of the hyperspectral illumination system and should be reduced.
## 6 Limitations and Challenges in Passkeys
### Account Revocation  
Since FIDO2 is based on decentralized authentication, if an authenticator is compromised, the user must manually and repeatedly disable all respective keys on each service [83]. There is, however, some academic work that is investigating a global key revocation standard, which would simplify these processes [83].  
Enterprise environments inherit some of the same barriers but with an amplified impact, as additional unique barriers exist for such environments. The current challenges for passkey adoption in enterprise environments are, namely, account delegation, recovery, and revocation. Usually, in large-scale enterprises, Identity and Access Management (IAM) practices are present, and these systems act as a single source of truth for access control all over the organization. The lack of a standardized way of recovery, delegation, and revocation of accounts might deter integration efforts [69; 85; 83].  
Additionally, the lack of standardization in user interfaces across different browsers and devices can lead to many pathways that the individual employee needs to navigate, requiring additional training [85]. A well-experienced Chief Information Security Officer (CISO) in a large non-governmental organization recommended a way to introduce such training through the gradual roll-out of security solutions like passkeys in a per-department setting with the corresponding onboarding of employees. He mentioned the onboarding should be done in very small groups to be able to tackle each suborganization's requirements and challenges with the policy's adoption [73].  
Finally, server software running on Windows and Linux operating systems can be difficult to integrate with FIDO2, whilst enterprises rely greatly on them [85]. Furthermore, adopting passkeys might be more challenging in enterprise environments, as complexities arise with integrating the technology into legacy IT systems and accommodating shared workstations, which are common in these environments [85].
## 4 Experiments and Results
### Experimental Methodology
#### 4.1.3 Image Processing  
Image processing applications are some of the most widely used serverless applications. The serverless image processing function is triggered when an image is stored or updated in the database or object storage. The image processing application used in this work is adopted from [69] and modified to be executed in an open-source serverless platform. As the deployment of the AWS image recognition collection has limitations prohibiting its execution in the edge and fog layers, we adapted the application to include simple image processing functions, namely metadata extraction, metadata transformation, metadata storage, and image resizing. Thus, our adapted application only consists of four image processing functions. The functions are chained together to process a request. In the cloud layer, we used step functions to create a workflow that connects all four functions. For the storage of metadata in the database, in the cloud layer, we used DynamoDB, and in the edge and fog layers, we used CouchDB, which is included in the OpenWhisk platform. We submitted 50 function invocations with a Poisson arrival rate of 0.5. In the cloud layer, a Python script that can invoke the step functions is hosted in an ec2 instance in the same region. We used the latency performance metric to measure the performance of the serverless platform in each layer. Latency directly measures the time taken for a serverless platform to respond to a request. In scenarios where user experience and responsiveness are critical, such as web applications or interactive services, latency is a fundamental metric. Latency is a widely accepted and standardized metric for the benchmarking and comparison of the performance of serverless platforms [70,71].
###### Abstract
We provide a comparative analysis of the routing protocols under each categorisation by considering various performance metrics, including delay, reliability, throughput and energy consumption. Further, under each categorisation, we provide open issues and challenges. * We provide a detailed background of WBASNs, including architecture, topologies, standards, application requirements for chronic diseases, the benefits and use of various frequency bands, comparative analysis of WBASN's available technologies, including LoRa and NB-IoTs, etc. Therefore, the essence of such a multi-aspect survey in WBASNs is beneficial to the research community as it provides recent trends on WBASNs. Figure 2 provides the taxonomy of this survey. The rest of the paper is organised as follows: Section 2 presents the background of WBASNs, including its architecture, requirements and existing technologies and standards. Section 3 introduces the proposed taxonomy of MAC protocols with categorisation and comparative analysis in terms of delay, reliability, throughput, mobility, interference and energy. Section 4 provides the categorisation of routing protocols, the comparative analysis of various recent protocols under each category. Section 5 provides a discussion about open issues and challenges. Section 6 concludes the survey paper.
## 3 Results and Discussion  
### Oxidative Stability of Groin, Jovl, and Trimming Shoulder Fats  
The results for the oxidative stability of groin fat, jowl fat, and trimming shoulder fat are presented in Table 2.  
Based on the results regarding the oxidative stability of pork fats at a temperature of 100 \({}^{\circ}\)C, the influence of the type of feed on the induction time in jowl fat and trimming shoulder fat can be observed. Fat extracted from the jowl of pigs fed dry feed was characterized by a longer induction time of oxidation (91.65 min) than fat from pigs offered liquid feed (89.05 min). In the case of trimming shoulder fat, an inverse result was observed. Fat extracted from the trimming shoulder of pigs fed a liquid diet was more oxidatively stable. There were no statistically significant differences in the oxidative stability of groin fat; induction time of oxidation in both cases (GF_L and GF_D) was approximately 108 min. In the oxidative stability test carried out at the temperature of 120 \({}^{\circ}\)C (Table 2), the influence of the type of feed on the oxidative stability of pork fat obtained from jowl and trimming shoulder was found. In the case of fat extracted from the jowl of pigs fed liquid feed, the induction time was longer (21.76 min) than in the fat of dry-feed pigs (20.70 min). The use of liquid feed had a positive effect on the oxidative stability of fat extracted from jowl and trimming shoulder by extending the length of induction time of oxidation. In groin fat, there was no dependance between the type of feed and the induction time of pork fat oxidation. Pork groin fat was less prone to oxidation than fats obtained from the jowl and trimming shoulder. The worst oxidative stability was observed in the case of jowl fat of pigs fed dry feed (20.70 min). In the oxidative stability test carried out at a temperature of 140 \({}^{\circ}\)C (Table 2), the influence of the feed system on the induction time of the oxidation process in the case of jowl and trimming shoulder fats was found. In the case of trimming shoulder fat, a shorter induction time of oxidation reaction was observed for fat samples of dry-fed pigs. An inverse result was demonstrated in pig jowl fat. The fat of pigs fed a liquid diet was characterized by a shorter induction time (4.04 min) compared to the fat of pigs offered dry feed (5.29 min). In the case of groin fat, no statistically significant differences were found between the oxidation induction time of fats from liquid- and dry-fed pigs. Based on the obtained results, it can be summarized that the influence of feed type on oxidative stability was found in pork fats extracted from jowl
## 3 UAV Path Planning Model Based on Q-Learning Strategy
### The Solution Framework for UAV Path Planning Based on Q-Learning  
Suppose that the state of the UAV motion is partially observed, and that there are some hidden states that can be observed in the system that satisfied the Markov attribute. Then this problem can be transformed into a Q-learning problem and described as follows.  
Let \(x_{k}=\left(\begin{array}{ccc}s_{k}&m_{k}&\mu_{k}&\sigma_{k}\\ \end{array}\right)\) be the system state of the UAV path planning system at time \(k\), where \(s_{k}\) denotes the states of the sensors; the position, velocity, acceleration, and detection information of the flight environment are included. \(m_{k}\) denotes the standard path for UAV, including the longitude, latitude, and altitude information of the path points. \(\left(\begin{array}{cc}\mu_{k}&\sigma_{k}\\ \end{array}\right)\) represents the state of the path planning, and is a standard Kalman filter, where \(\mu_{k}\) and \(\sigma_{k}\) are the posterior mean vector and covariance matrix, respectively.  
If the acceleration and bank angle of the UAV are chosen as the controlled variables, then the action at time \(k\) can be represented as \(u_{k}=\left(\begin{array}{cc}a_{k}&\phi_{k}\\ \end{array}\right)\), where \(a_{k}\) and \(\phi_{k}\) denote the acceleration and the bank angle at time \(k\) of the UAV, respectively.  
The effect of the action that the UAV adopts should be considered in the state transition of the system. Let \(m_{k}^{p}\) and \(s_{k}^{p}\) denote the position of the standard path point and the current position of the UAV, respectively; then the observation function of the system can be represented as follows:  
\[z_{k}^{s}=H_{k}s_{k}^{p}+w_{k}, \tag{1}\]  
where \(H_{k}\) denotes the observation model and \(w_{k}\) is the random observation error. The distribution of \(w_{k}\) depends on the position of the UAV \(s_{k}^{p}\) and the position of the standard path point \(m_{k}^{p}\), and the system frame of the UAV path planning based on POMDP is shown in Figure 4.  
Figure 4: Path planning system frame for UAV based on Q-learning.  
Figure 3: The structure of the motion strategies problems.
# A Neural Network Propagation Model for LoRaWAN and Critical Analysis with Real-World Measurements
## 5 Discussion
Similarly, in addition to providing the ANN with the number of walls blocking the LoS, the total optimized attenuation of these walls are extracted from the COST231 model and passed as an input to further assist the network's learning process. Above all, rather than entirely training the ANN to predict the propagation, it was trained to correct the inaccuracies of the COST231 model and improve upon its performance. With this approach, training is carried out using 270 data samples from the eighth floor, and then tested for the collected measurements on the seventh floor. This approach made the training process easy, quick, and needless of an intensive data collection. Packet loss was observed at some of the locations during the data collection. This was compared against the fading standard deviation (\(\sigma\)) and RSSI; however, no particular correlation was found between them. This message loss might have been due to frequency interference, a destructive multi-path, or even the mobile data connection of LoRaWAN to the data server using a User Datagram Protocol (UDP).
# FFT-Based Scan-Matching for SLAM Applications with Low-Cost Laser Range Finders
## 5 Experiment  
### Experimental Facilities and Settings  
The experimental facilities are shown in Figure 6. The left is the Robot, the right is RPLidar-A1. RPLidar-A1 is a low-cost LRF sensor with 360\({}^{\circ}\) coverage field and range of up to six meters. The key parameters of RPLidar-A1 are listed in Table 1. The computer used was a laptop, with intel i3 CPU and 2 GB memory.  
The experimental data were obtained by RPLidar-A1 under different indoor environments, including office, corridor, laboratory room, etc., as shown in Figure 7. The ground truth of real-world experiments was collected by manual measurement of key positions.  
Different experiments were undertaken to evaluate the performance of matching accuracy, matching success rate, computational speed, and moving performance. The methods for comparison included NDT [37], ICP [26], original FFT [48], original FFT-ICP1 [47], and the FFT-ICP2 of this paper.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Measuring distance** & **Ranging accuracy** & **Angle range** & **Angle resolution** & **Frequency** & **Price** \\ \hline
0.15–6 m & 0.001 m & 0\({}^{\circ}\)–360\({}^{\circ}\) & 1\({}^{\circ}\) & 5.5 Hz & \$73 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The key parameters of RPLidar-A1.  
Figure 5: Proposed Fast Fourier Transform-Iterative Closest Point (FFT-ICP) scan-matching frame-work.  
In the experiments, for FFT processing, the maximum range of the scan data for converting to an image was \(d_{max}\) = 3.5 m. The image size for original FFT [48] was set to 256 \(\times\) 256, while the image size for FFT-ICP1 [47] and FFT-ICP2 was set to 128 \(\times\) 128.