esa-sceva/satcom-chunk-collection huggingface dataset:
# Error Distribution Pattern Analysis of Mobile Laser Scanners for Precise As-Built BIM Generation
## 3 Experiment Design and Analysis Method
### Analysis Method of Error Distribution Patterns in MLS Point Clouds
#### 3.4.1 Intentions of the Analysis Method  
In the proposed analysis method, reference planes were obtained from the TLS point clouds, and the test data were acquired from the MLS point clouds. ICP registration was applied to align the MLS point clouds with the reference planes. This algorithm iteratively rotates and translates the MLS scan data to minimize the RMSE, thereby aligning the two datasets. Under such conditions, the expected result of ICP registration between a reference plane and a point cloud would place the reference planes near the region with the highest point density in the point clouds. In other words, when performing ICP registration between a reference plane and the point clouds, the alignment process tends to ignore the underlying error distribution pattern in the data due to its focus on minimizing RMSE. To address this, the mock-ups used in this experiment were designed in the form of columns, which have a closed-shape in xy-plane, ensuring that the influence of point clouds on all four sides is evenly reflected in the RMSE calculation, thereby allowing the inherent error distribution patterns of the MLS to emerge. Based on the error distribution pattern inherent in the MLS point clouds, the expected outcome of ICP registration in this configuration is shown in Figure 5. The Figure 5a-crepresent cases where the point clouds are primarily distributed behind the surface, near the surface, and in front of the surface of the object, respectively.  
Figure 4: Proposed method for analysis of error distribution pattern in MLS point clouds.  
If the aforementioned conditions and assumptions are satisfied, the RMSE calculations for opposing planes during ICP registration become complementary. When considering the MLS point clouds corresponding to two opposing planes, it is advantageous for the RMSE calculation to position the densest regions of each plane as close as possible to the reference plane. However, when the densest region of one plane is aligned with the reference plane, the RMSE value for the opposite plane increases significantly. Accordingly, the error distribution patterns of opposing planes should be similar. Therefore, the authors believe that the error distribution patterns of MLS point clouds are observed throughout the proposed analysis method. Additionally, the distances from each point of MLS point clouds to reference planes play a representative role in quantifying error values.
## 4 Results and Analysis
### Comparative Analysis of ML Models with TextBlob Sentiments Using Balanced Dataset
#### 4.5.1 Performance of Models on Balanced Dataset Using Under-Sampling  
As shown earlier, the proposed approach achieved the best performance when the concatenated features and all lexicons on Arab TextBlob sentiments were used. To investigate the robustness of the model and to avoid any overfitting, further experiments were performed using a balanced dataset with a random under-sampling technique. The obtained results of the under-sampled data are shown in Table 30.  
The results show that the performance of the selected models has been degraded. Among all the ML classifiers, the NB classifier had the worst accuracy, of 82.11%. The reason behind this degradation is the reduction in the size of the dataset. In contrast to the findings shown in Table 28, the bagging classifier had the highest accuracy and F1-score, with values of 88.36% and 0.882, respectively. In addition, the performance of SVM was negatively affected.
# Prediction of Tooth Profile Deviation for WEDM Rigid Gears Based on ISSA-LSSVM
## 3 Experimental Section and Methods
### Total Tooth Profile Deviation Measurement  
The machined rigid gear, a small modulus multi-tooth internal gear, is shown in Figure 4c. Small module gears typically have small tooth slot clearance and poor tooth rigidity. Conventional contact measuring instruments are inefficient and difficult to use for measuring small module gears [36]. This study uses a fully automated image measuring machine (MED-5040 CNC, Leader Metrology Inc, MD, America) shown in Figure 5 to measure the total tooth profile deviation of rigid gears. This machine utilizes machine vision inspection technology, enabling accurate gear measurement and online detection of gear defects. The measurement results are presented in Table 6, with the total tooth profile deviation denoted by \(F_{\alpha}\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Test Number**} & **Peak Current** & **Pulse Width** & **Pulse Interval** & **Tracking** & \multirow{2}{*}{\(F_{\alpha}\) **(mm)**} \\  & **(A)** & **(us)** & **(us)** & **(Hz/s)** & \\ \hline \(1\) & \(1\) & \(8\) & \(6\) & \(100\) & \(0.151\) \\ \(2\) & \(1\) & \(16\) & \(7\) & \(150\) & \(0.149\) \\ \(3\) & \(1\) & \(24\) & \(8\) & \(200\) & \(0.154\) \\ \(4\) & \(1\) & \(32\) & \(9\) & \(250\) & \(0.157\) \\ \(5\) & \(1.5\) & \(8\) & \(7\) & \(200\) & \(0.146\) \\ \(6\) & \(1.5\) & \(16\) & \(6\) & \(250\) & \(0.155\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Orthogonal test results of total tooth profile deviation.  
Figure 5: Image measuring machine and measuring procedure for total tooth profile deviation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Level**} & **Peak Current/(A)** & **Pulse Width/(\(\mu\)s)** & **Pulse Interval/(\(\mu\)s)** & **Tracking/ (Hz/s)** \\ \hline \(1\) & \(1\) & \(8\) & \(6\) & \(100\) \\ \(2\) & \(1.5\) & \(16\) & \(7\) & \(150\) \\ \(3\) & \(2\) & \(24\) & \(8\) & \(200\) \\ \(4\) & \(2.5\) & \(32\) & \(9\) & \(250\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Input parameters and levels.
# Posterior Probability-Based Symbol Detection Algorithm for CPM in Underwater Acoustic Channels
## 5 Sea Trial  
A sea trial of MSK UWA communication was conducted during winter in the South China Sea to verify the proposed algorithms. Figure 8 shows that the average depth of the test area was 208 m, and the communication distance was approximately 5.3 km. A transducer operating at an 80 m depth transmitted MSK signal at three distinct source levels of 196, 190, and 184 dB. The hydrophone positioned at a 97 m depth received the signal.  
Figure 8: Sea trial program.  
Figure 7: BER for GMSK with PS-FDE: (**a**) ChA; (**b**) ChB; (**c**) ChC.  
Figure 9 presents the measured structure of the UWA channel, which was obtained by applying the OMP algorithm to the MSK signal. The maximum delay of the channels was approximately 23 ms, which could be divided into four multiple-path clusters. Throughout the course of the sea trial, 210 groups of MSK signals were transmitted at 6 kHz center frequency, 4-8 kHz transducer bandwidth, 2 kbps communication rate, and 256 bits per frame transmission information sequence. FDE and PS-FDE were used to receive the MSK signal, wherein the FDE error bit was equivalent to that of the PS-FDE at \(k\) = 0.  
Table 5 presents the number of bit errors in the received signal at different signal-to-noise ratios (SNRs) within the 4-8 kHz bandwidth. Variations in source levels and propagation losses during UWA communication led to differences in the computed SNR values. The BER of different algorithms exhibited a significant decline as SNR increased. At \(k\) = 0, the performance of PS-FDE was equivalent to that of traditional FDE (FDE with simple phase detection). Compared with traditional FDE, PS-FDE reduced the number of bit errors by 10, 34, and 44 at \(k\) = 1, \(k\) = 2, and \(k\) = 3, respectively, demonstrating its effectiveness. As the number of information merging iterations \(k\) increases, the amount of effective symbol information obtained in the final merging process decreases. Consequently, the detection gain approached saturation, leading to a gradual reduction in performance improvement.
# Load-Balanced Dynamic SFC Migration Based on Resource Demand Prediction
## 3 System Model and Problem Description
### Network Model  
We consider an NFV network architecture with three layers as depicted in [11]. As shown in Figure 1, physical nodes are connected by links in the physical layer. VNFs are instantiated on physical nodes that supply the necessary resources in the VNF layer. The various NF types that comprise the SFC are deployed on matching VNF types at the application layer.  
The physical network is modeled as a fully connected undirected graph \(G=(V^{s},E^{s})\), where \(V^{s}\) is the set of underlying nodes and \(E^{s}\) is the set of physical links between nodes. The underlying nodes have a set of resource types to support VNFs, such as CPU, memory, and cores. Here, \(C_{i}^{S}\) represents the maximum CPU resource capacity of the \(i\)-th node \(v_{i}^{s}\in V^{S}\), where each unit of CPU resource represents the resources required to process a data packet. The memory resource capacity of the \(i\)-th node is \(M_{i}^{S}\), represented in available megabytes. The underlying link directly connecting nodes \(v_{i}^{s}\) and \(v_{j}^{s}\) is represented as \(e_{ij}^{S}\in E^{s}\), the physical link bandwidth is \(BW_{ij}^{S}\), and the link delay is represented as \(L_{ij}^{S}\).
## 2 Materials and Methods
### Experiment
#### 2.1.4 SSGT Set Up  
HMX-based booster was tested at different temperatures through SSGT to verify its shock sensitivity. After the detonator initiated the donor explosive, the detonation wave was attenuated by the gap and then acted on the acceptor explosive. A witness plate of steel material was installed at the bottom of the acceptor to facilitate the Go/No-go judgment by observing the dents of the steel witness plate and the fracture steel sleeve of the acceptor. The length of gap substance was varied to observe the 50% critical gap thickness, namely G\({}_{50}\), until the acceptor charge detonated under the condition of SSGT. SSGT method in this paper mainly adopted the national military standard GJB2178.1A-2005 (test method of safety for booster) [32]. The explosives were confined within a circular steel sleeve with an inner diameter of approximately 5.15 mm and a thickness of 10.125 mm. The donor explosive was cyclomethylenetrinitramine (RDX) with an initial density of 1.635 g/cm\({}^{3}\), which was pressed directly into the steel sleeve seven times. The acceptor was HMX-based booster, and its initial density was 1.700 g/cm\({}^{3}\). Seven HMX-based booster tablets with a size of \(\Phi\) 5.09 mm\(\times\)5.45 mm were pressed and filled into the steel sleeve. The height of the donor and acceptor should be kept the same at 38.1 mm, while the gap thickness was varied by stacking PMMA discs. SSGT configuration is shown in Figure 4.  
During SSGT at non-ambient temperature, HMX-based booster acceptor charges were preheated or cooled firstly, and the detonator, donor charge, PMMA gap, and witness plate was assembled together in descending order and placed in the explosion box in advance. The position of the acceptor charge was first located in the empty steel sleeve. After that, the acceptor charge was quickly removed from the oven or refrigerator and placed in a proper position for assembly, and the detonator was quickly detonated.  
Figure 4: Small scale gap test configuration.  
Figure 3: Temperature calibration experimental assembly.
# Personalized Context-Aware Authentication Protocols in IoT
## 3 Results  
As a result of all of the above state-of-the-art analysis and the preliminary queries into cognitive cryptography, a concept of a data vector of digital footprints was proposed. All of the mentioned datatypes can be joined into a structured data stream, with the possibility of addressing any part of it to access the data stored within, denoting the datatype and size of the block [20]. Thus, a sequence can be generated that is partially accessible to all of the devices in the personal network and dependent on their computing power and the ability to work on a specific data cell. The order and content of the sequence are personalized to the individual and depend on the most ubiquitous datatype, the consent given, and the devices available. An example vector can be seen in Figure 1. Every potential characteristic that can be used during the authentication process is assigned an address.  
The matrix structure is then populated to the IoT devices that are expected to be able to authenticate the individual. Every device marks the parts of the structure that are able to be processed locally by the device according to its hardware and sensor specifications, e.g., does it have a fingerprint scanner, a GPS, a camera, or local photo storage? Such information is held by the device as its local authentication ability profile (LAAP).  
It also has to be noted that every device in the proposed algorithms only has access to its own sensor output/data storage and data that can be freely accessed through internet connectivity (in the case of social media). Thus, there is no centralized database (which would defeat the main advantage of IoT) for the data about an individual, and every device uses its own security methods to protect the data at rest (provided by the vendor). No personal data are sent over an unsecured network. Only random fragments of the datastream (Figure 1), challenge results, or hashes are to be used as data in transit.
# Internet of Things-Based Healthcare Systems: An Overview of Privacy-Preserving Mechanisms
## 3 Stratifying Privacy-Preserving Solutions
### User Awareness and Empowerment  
This category emphasizes the importance of user education, awareness, and control in preserving privacy in IoT-based healthcare systems. By educating individuals and providing them with tools to control their PHI, these measures enable users to actively participate in privacy protection. Key aspects include the following:  
1. User Education and Awareness Programs: These programs aim to establish knowledge and awareness among individuals regarding data processing practices, privacy risks, and their rights regarding PHI. Such programs can delegate the responsibility to the users to make knowledgeable decisions and apply proactive measures to assure privacy compliance [17].
2. User-Controlled Privacy Settings: User-controlled privacy settings enable individuals to manage their privacy preferences and customize the level of data sharing and access permissions. Users can control how their personal health information is used and shared.
3. Privacy-Enhancing Technologies: Such technologies comprise tools, techniques, and interfaces that enable users to protect their privacy while benefiting from IoT-based healthcare services. They allow individuals to preserve their privacy without compromising the functionality and utility of the system [17].  
The above five core components are the central areas of focus in addressing privacy concerns in IoT-based healthcare systems. These components are widely investigated and challenging due to their pivotal role in ensuring that healthcare data are kept private. Because of their complexity, it is necessary to invest in focused research efforts to develop robust solutions that comply with regulations while effectively balancing privacy with the utility of healthcare data.
# Development of Ultrasonic Pulsed Plasma Jet Source for Remote Surface Treatment
## 4 Conclusions  
This study describes the development of a supersonic pulsed plasma jet device capable of long-distance and high-speed processing and investigates the basic characteristics of the device for surface treatment applications, mainly in the material and medical fields. The developed device was equipped with a mechanism to transport the active species in the plasma to the object to be treated by letting the generated high-density plasma outward with a supersonic pulse jet. The high-density active species in the plasma can be irradiated to distant targets before they are deactivated. In the case of short distances, more active species can reach the target; thus, faster processing can be expected. If the constant flow velocity is increased, the discharge power density per flow rate decreases, and the treatment effect decreases. In addition, gas consumption increases; therefore, a short-pulse method was used. Observation of the plasma by a high-speed camera confirmed that the plasma emission intensity increased, and the extension distance increased when the plasma was discharged by a pulsed jet. The velocity of the supersonic pulsed plasma jet was measured using the schlieren method, and it was found that the pulsed plasma jet was a short knife jet. The maximum Mach number was calculated to be Mach 1 or higher for helium plasma, Mach 1.6 for argon plasma, and Mach 1.7 for nitrogen, carbon dioxide, and air plasma. Power measurements confirmed that the helium and argon plasmas during the pulsed jet increased in power, whereas the carbon dioxide plasma decreased in power. The difference in the power is considered to depend on the breakdown voltage. The above results suggest that the reachable distance of the active species is extended, and the efficiency of surface treatment by plasma is improved. The effect of the plasma gas type on the hydrophilic effect was investigated. The maximum hydrophilic effect was observed when the nitrogen supersonic plasma was irradiated at 13 Hz for 10 ms, and the contact angle was reduced by a maximum of 14\({}^{\circ}\). Additionally, it was found that nitrogen supersonic plasma was effective in improving hydrophilicity for short-distance treatment and helium supersonic plasma for long-distance treatment.  
Conceptualization, T.O.; investigation, T.O. and D.O. and T.I. and H.M.; project administration, A.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data available in a publicly accessible repository.  
We thank [PERSON] of Kato Koken Co., Ltd., who helped us with the schlieren measurements and analysis.  
Figure 5: Relationship between irradiation distance and hydrophilic effect: (**a**) helium plasma; (**b**) argon plasma; (**c**) nitrogen plasma; (**d**) air plasma; (**e**) carbon dioxide plasma.
## 3 The Proposed Method
### Data Extraction and Image Recovery
#### 3.4.3 Identifying Information Extraction  
To illustrate the extraction process of the identifying information embedded in the transitional image pixel \(\mathbf{IR}(i,j)\), consider the extraction of \(\mathbf{IR}(i,j),a_{1}(i,j),\ldots,a_{r-1}(i,j)\) as per Equation (14). Subsequently, the key \(K_{p}\) defines the mapping \(e_{ij}\), enabling the derivation of \(f_{k}(i,j)\) from \(a_{1}(i,j)\). By following the procedure outlined in Equation (17), the encrypted identifying information \(m_{1}\) can be obtained:  
\[ms_{1}=a_{1}(i,j)-f_{k}(i,j) \tag{17}\]  
Following this, the application of Equation (10) enables the computation of \(m_{52}\), \(\ldots\), \(ms_{r-1}\). Subsequently, decrypting the data using the key \(K_{d}\) reveals part of the identifying information \(\mathbf{md}\). This operation is repeated to extract all the identifying information, and it is compared with the \(\mathbf{md}\) prior to embedding. Consistency denotes a secure transmission environment, whereas discrepancies point towards a potential data tampering attack during data transmission.  
The receiver can independently perform secret data extraction, image recovery, and identifying information extraction based on possessing the key. These operations can be conducted separately.
## 2 Materials and Methods
### Chemicals  
Methanol (99.9% purity, CAS No. 67-56-1) and acetic acid (99.9% purity, CAS No. 64-19-7) were purchased from Merck (Darmstadt, Germany) and used without further purification. Standard polyphenols: rosmarinic acid (CAS No. 20283-92-5), caftaric acid (CAS No. 67879-58-7), gentisic acid (CAS No. 4955-90-2), chlorogenic acid (CAS No. 327-97-9), caffe acid (CAS No. 331-39-5), p-coumaric acid (CAS No. 501-98-4), ferulic acid (CAS No. 537-98-4), sinapic acid (CAS No. 530-59-6), hyperoside (CAS No. 482-36-0), isoquercitrin (CAS No. 482-35-9), rutin (CAS No. 153-18-4), myricetin (CAS No. 529-44-2), fisetin (CAS No. 345909-34-4), quercetin (CAS No. 522-12-3), quercotel (CAS No. 117-39-5), luteolin (CAS No. 491-70-3), kaempferol (CAS No. 520-18-3) and apigenin (CAS No. 520-36-5) were purchased from Sigma-Aldrich (Germany). Folin-Ciocalte reagent (FC), gallic acid (GA, CAS No. 149-91-7), 2,2-diphenyl-1-picrylhydrazyl (DPPH, CAS No. 1898-86-4), ascorbic acid (AA, CAS No. 50-81-7), indomethacin (CAS No. 53-86-1) and sodium dodecyl sulfate (SDS, CAS No. 151-21-3) were purchased from Sigma-Aldrich (Steinheim, Germany). Sodium carbonate anhydrous (CAS No. 497-19-8) was obtained from VWR International bvba (Leuven, Belgium). All used reagents were of analytical grade.
## 3 Results and Discussion
### Denitration Rates of the Three Biochars  
#### 3.2.1 Effect of Reaction Temperature  
Figure 3 shows the denitration rate of the three biochars at different reaction temperatures. From Figure 3a, at 800 \({}^{\circ}\)C, 850 \({}^{\circ}\)C, and 900 \({}^{\circ}\)C, the initial denitration rates of RHA were 87.7%, 92.7%, and 89.7%, respectively. The denitration rates of RHA were higher at the start of the experiment in the three temperature conditions, which indicates that RHA has high reduction ability at high temperatures. As shown in Figure 3a, the denitration rate of RHA was the highest at the temperature of 850 \({}^{\circ}\)C and decreased slowly with time. When the temperature increased to 900 \({}^{\circ}\)C, the denitration rate of RHA decreased. The reason for this may be that at 850 \({}^{\circ}\)C, the reduction of NO by C reached an optimal state. At high temperatures, carbon oxides are more likely to be produced [22]. When the temperature was 900 \({}^{\circ}\)C, more carbon oxides were generated. On the one hand, the carbon oxides produced  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Surface Area/m\({}^{2}\)g\({}^{-1}\)** & **Pore Volume/cm\({}^{3}\)g\({}^{-1}\)** & **Pore Diameter/nm** \\ \hline RHA & 354.1 & 0.24 & 5.02 \\ BC & 9.2 & 0.02 & 4.00 \\ SC & 148.2 & 0.12 & 3.23 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structural parameters of the three carbon materials. RHA: rice husk ash, BC: bamboo charcoal, SC: straw charcoal.  
Figure 2: SEM images of (**a**) RHA, (**b**) BC, (**c**) SC. The magnification for all samples is 100 k.  
consumed C and O, and on the other hand, it suppressed the reduction reaction. Figure 3b shows the denitration rate of BC at different temperatures. The initial denitration rates of BC were 70.9%, 81.6%, and 98.9%, corresponding to the environmental temperatures of 800 \({}^{\circ}\)C, 850 \({}^{\circ}\)C, and 900 \({}^{\circ}\)C, respectively. The differences in the initial denitration rates at the three reaction temperatures were larger compared to those of RHA, and the highest denitration rate was measured at 900 \({}^{\circ}\)C. From the data, it can be seen that BC is a good reduced of NO when the temperature is above 850 \({}^{\circ}\)C. This may be due to the fact that BC contains more C-O bonds and (NCO)x bonds than RHA, which are promote the reduction reaction. These chemical bonds are more active at high temperatures, so the denitration rate was the highest at 900 \({}^{\circ}\)C. Figure 3c shows the denitration rate of SC. The initial denitration rates at 800 \({}^{\circ}\)C, 850 \({}^{\circ}\)C, and 900 \({}^{\circ}\)C were 85.3%, 88.0%, and 91.3%, respectively. The differences in the denitration rates of SC at different temperatures were small. SC displayed a higher reducing effect on nitrogen oxides at higher temperatures. In summary, the optimum reaction temperatures for RHA, BC, and SC were 850 \({}^{\circ}\)C, 900 \({}^{\circ}\)C and 900 \({}^{\circ}\)C, respectively.