esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
When the initially calculated paths need to be changed to avoid collisions, their associated task sequences will also be reassigned. [PERSON] et al. [21] designed an integrated method where task assignment choices are determined by actual delivery costs. The actual path cost is considered when assigning tasks to agents for improving the quality of task assignment. However, these methods do not consider the precedence constraints between tasks and may not apply to the scenario of smart manufacturing. To make up for these shortcomings, in this paper, we propose an integrated task- and path-planning approach for mobile robots in smart factories. This approach can solve task assignment and path planning in a joint way while considering the precedence constraints of tasks and conflict-free constraints of paths. The core ideas of the approach are the Looking-backward Search Strategy (LSS) and Regret-based Search Strategy (RSS) developed for the process of task assignment, which can further reduce the total operating time and the consumed energy of machining centers. Specifically, the Regret-based Search Strategy (RSS) is inspired by the regret mechanism [23; 24; 25; 26] in the task assignment process. In the regret mechanism, instead of greedily choosing the current best solution, both the best and the second-best solutions are considered, which facilitates finding a more efficient solution from a holistic point of view. For example, [PERSON] et al. [25] used the regret mechanism to assign tasks to mobile robots considering both the smallest and second smallest travel distances. Similarly, [PERSON] et al. [26] used the idea of regret for determining the weighted sum of the best candidate and second-best candidates to visit each customer in a homecare staff scheduling problem. Our proposed RSS also adopts this mechanism and reduces the idle time of machining centers in smart factories. Our approach preliminarily solves the coupling of task assignment and path planning while improving the operational efficiency and reducing the energy consumption of the smart factory. It can contribute to the practical deployment of multi-mobile robot systems in smart factories and promote the development of more advanced high-efficiency and energy-saving manufacturing modes. This paper is structured as follows. Section 2 introduces our integrated task assignment and path-planning approach. Section 3 presents a simulation experiment validating the proposed approach and discusses the experimental results. Section 4 provides a summary of our work and suggests future research directions.
# Urban Quarry Ground Vibration Forecasting: A Matrix Factorization Approach
## 4 Measurement of PPV
### Collection of Ground Vibration Data
Care must be taken when setting this range; if the set range is exceeded, it becomes impossible to measure the peak vibration values. A range of 500 mV was set for collecting ground vibration data. Meanwhile, the filter excludes vibrations of a frequency higher than the set value, which helps in avoiding noise (high-frequency vibrations) and simplifying data processing. In this study, a filter of 500 Hz was set, so vibrations above 500 Hz were not measured. In the memory settings (Figure 4c), one sets the sampling interval (sampling frequency), block size, and measurement duration. The sampling frequency indicates the interval for measuring vibration values, which affects the vibrational values that can be measured. Generally, the sampling frequency must be set to a value at least twice as high as the frequency one wishes to measure. Ground vibrations due to blasting are known to fall between 10 and 200 Hz and are known to be high-frequency waves compared to natural earthquakes. Considering these facts, the sampling frequency was generously set to 10 kHz for the collection of ground vibration data. The block size indicates how many times the measurement time is divided, and the measurement duration is determined by the sampling frequency and block size. In the trigger settings (Figure 4d), one can set the timing (trigger) to start the measurement in percentage terms. Specifically, measurement starts when a vibration larger than the percentage value of the range set in the amplifier settings is detected. To ensure  
Figure 3: Schematic diagram of experimental devices. no vibrations before the start of the measurement are missed, a pre-trigger can be set in percentage terms. Vibrations occurring a percentage of the measurement time set in the memory settings before the triggers are recorded. In this study, the trigger and pre-trigger were set to 8% and 10%, respectively. After the measurement, the data were saved to a computer in comma-separated value (CSV) format. The location where the vibration measurement took place was also recorded on a Google Maps map, and the latitude and longitude coordinates of that measurement point were recorded simultaneously.
# Review and Evaluation of MAC Protocols for Satellite IoT Systems Using Nanoatellites
## 5 MAC Protocols for Satellite IoT and M2M
### Interference Cancellation-Based Protocols
#### 5.3.5 Enhanced Spread Spectrum Aloha (E-SSA)  
This protocol is similar to SSA on the transmitter side. On the receiver side, enhanced spread spectrum Aloha (E-SSA) employs an iterative soft interference cancellation (ISIC) algorithm with a sliding window that captures the messages received on an unsynchronized channel. The main difference with the previous protocols is that E-SSA does not require sending multiple copies of packets, nor synchronization; thus, achieving a greater efficiency with a reduced complexity. The E-SSA protocol has been designed for integrated satellite/terrestrial mobile systems [5; 68]. Figure 12 shows the operation of this protocol.  
In terms of performance, the maximum normalized throughput reported for this protocol is \(S=1.2\) for a channel load of \(C=1.25\). When the \(PLR=10^{-3}\), E-SSA is able to operate with a normalized load of \(C=1.12\), assuming that the power imbalance of the transmitting nodes is equivalent. As opposed to SSA, in E-SSA, if a power imbalance of \(\sigma=3\) dB is assumed among nodes, the performance improves considerably, achieving a normalized load of \(C=1.9\) under a \(PLR=10^{-3}\).  
The interference-cancellation protocols presented in this section are designed mostly to solve the multiple access for the uplink of satellite systems. However, most of the control information required by the protocols is actually acquired/exchanged on the downlink. For example, a correct channel estimation is a key element for the proper operation of E-SSA. Such an estimation takes place over the downlink. Similarly, the code sharing process of CSA also takes place on the downlink. In half-duplex systems, the interference-cancellation based protocol's feasibility requires careful examination.  
Regarding the suitability of this category for IoT services, it can be mentioned that despite the great performance reported for these protocols, the complexity of their correct implementation makes them hard to adapt given the capabilities of CubeSats in a LE orbit. Among the reviewed protocols, E-SSA stands out in performance compared to the others, and significantly reduces the complexity at the ground terminal; however, it is still highly demanding of resources on the nanosatellite side.
## 2 Materials and Methods
### Participants  
The inclusion criteria were as follows: individuals under 18 years of age; head in the same position during both CBCT scans; high-quality CBCT scans; appropriate total length of treatment time; full field of view of the maxillofacial region; no cold, allergies, or inflammation in the upper airway during the CBCT scan; no previous history of orthodontic treatment; and no history of craniofacial surgical procedures. Based on the pilot study, the sample size for the paired \(t\)-test for the experimental group was calculated to require a minimum of 7 patients. The sample size calculation was based on a Significance Level (alpha) of 0.05 and a power of 0.95. In the experimental group, a total of 23 initial patient records were evaluated, and 12 of them were excluded for not meeting the study's inclusion criteria. The final experimental group was composed of \(n=11\) pediatric patients(4 males, 7 females) with a mean age of (9.13 \(\pm\) 0.51 years [mean \(\pm\) SE]). All participants underwent maxillary expansion using Invisalign(r) clear aligners (Align Technology, San Jose, CA, USA) as part of their orthodontic treatment plan with a mean treatment time of (13.43 \(\pm\) 0.74 months). All participants had a CBCT at the start of the treatment (T1) and another CBCT at the end of the treatment (T2) as part of their clinical treatment protocol. Another 12 patients had two CBCT scans as part of their proposed treatment but did not start the treatment at T1 due to financial or personal reasons. They came back for treatment at a similar T2 to the experimental group and were considered the control group. In total, 5 out of 12 initial patients in the control group were excluded for the same reasons that are mentioned above in the exclusion criteria. The final control group consisted of n = 7 patients (5 males, 2 females) with a mean age of (10.20 \(\pm\) 0.54 years). The mean time between the CBCT scans of the control group was 12.34 \(\pm\) 1.01 months. The CBCT scans for both groups were performed for clinical diagnosis and treatment planning using the i-CAT FLV V-Series (the model number is 1.009 9472) scanner (Dexis, Arlington, VA, USA). The pixel size of these CBCT scan images was 0.3 mm with a resolution of 536 \(\times\) 536 pixels per slice. The number of slices for the CT scan data sets was 440 and the slice thickness was 0.3 mm. To ensure the precision and consistency of CBCT imaging, participants were instructed to maintain the same steady head position, breathe naturally, and avoid exaggerated respiratory movements or swallowing, thereby maintaining uniform airway conditions. Approval was granted by the University of Alberta Health Ethics Review Board (protocol number: Pro 00047506). All patients signed a consent form for their records to be used for research purposes. All participants were recruited between September 2020 and February 2022 at the local clinic.
# Guided Particle Swarm Optimization for Feature Selection: Application to Cancer Genome Data
## 4 Experiments and Setup
### Dataset Description  
Twelve datasets have been used for the experiments. These have been extracted from the Cancer Genome Atlas (TCGA) [24]. TCGA is a pioneering cancer genomics initiative, and has molecularly characterized more than 20,000 primary cancer samples and corresponding normal tissues across 33 cancer types. This collaborative project, launched in 2006 by the National Cancer Institute (NCI) and the National Human Genome Research Institute, brought together experts from various fields and institutions.  
Over the course of twelve years, TCGA produced more than 2.5 petabytes of data, encompassing genomic, epigenomic, transcriptomic, and proteomic information. This vast dataset, which has already contributed to advancements in cancer diagnosis, treatment, and prevention, continues to be publicly accessible to the research community.  
The twelve datasets used are given in Table 1. As one can see, the number of features vary between 35,924 to 44,909. Since we are investigating feature selection, these datasets are considered to be very large and appropriate for our experiments. The dataset abbreviations stand for Cholangiocarcinoma (CHOL), Colon Adenocarcinoma (COAD), Head and Neck Squamous Cell Carcinoma (HNSC), Kidney Renal Clear Cell Carcinoma (KIRC), Kidney Renal Papillary Cell Carcinoma (KIRP), Liver Hepatocellular Carcinoma (LIHC), Lung Squamous Cell Carcinoma (LUSC), Prostate Adenocarcinoma (PRAD), Stomach Adenocarcinoma (STAD), Thyroid Carcinoma (THCA), and Uterine Corpus Endometrial Carcinoma (UCEC). All datasets are binary with two classes: cancer or not cancer.
# Sildenafil Citrate Liposomes for Pulmonary Delivery by Ultrasonic Nebulization
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \multicolumn{6}{c}{**Influence of Nebulization on the liposomes Stability (EE\%)**} \\ \hline \multicolumn{2}{c}{**Liposomes**} & \multicolumn{2}{c}{**0 min**} & **5 min** & **10 min** & **15 min** \\ \hline \multirow{4}{*}{**Without Vit E TPGS**} & **Fresh** & 89.77 \(\pm\) 7.64 & 85.10 \(\pm\) 0.64 & 83.56 \(\pm\) 3.04 & 77.78 \(\pm\) 0.89 \\  & **S** & 89.65 \(\pm\) 1.02 & 87.35 \(\pm\) 0.88 & 82.73 \(\pm\) 1.28 & 79.47 \(\pm\) 1.02 \\  & **T** & 89.95 \(\pm\) 2.15 & 86.90 \(\pm\) 0.50 & 81.23 \(\pm\) 4.29 & 78.15 \(\pm\) 1.91 \\  & **S + T** & 88.95 \(\pm\) 0.30 & 87.06 \(\pm\) 0.39 & 83.04 \(\pm\) 0.88 & 79.89 \(\pm\) 1.79 \\ \hline \multirow{4}{*}{**With Vit E TPGS**} & **Fresh** & 80.30 \(\pm\) 11.03 & 76.76 \(\pm\) 0.52 & 69.34 \(\pm\) 1.16 & 59.24 \(\pm\) 1.61 \\  & **S** & 88.77 \(\pm\) 0.22 & 85.84 \(\pm\) 0.92 & 84.06 \(\pm\) 0.97 & 82.20 \(\pm\) 1.13 \\ \cline{1-1}  & **T** & 88.57 \(\pm\) 0.59 & 85.94 \(\pm\) 0.43 & 82.86 \(\pm\) 0.53 & 81.12 \(\pm\) 1.43 \\ \cline{1-1}  & **S + T** & 88.39 \(\pm\) 0.25 & 86.05 \(\pm\) 1.05 & 84.15 \(\pm\) 1.04 & 81.82 \(\pm\) 0.89 \\ \hline \end{tabular}
\end{table} 
Table 3: EE% of nebulized liposomes before lyophilization (Fresh), and after lyophilization with sucrose (S), with trehalose (T), or with the mixture (S + T). Figure 3: TEM images of the fresh (**A**) and the rehydrated liposomes (**B**). The viscosity of the samples did affect nebulizer performance. A higher DR coupled with a lower NE was observed for samples with a lower viscosity (fresh liposomes without additives) as compared to those with a higher viscosity (lyophilized liposomes with sucrose, trehalose, or sucrose and trehalose). Statistical differences among samples were only found for NE (\(p\) = 0.0126).
## 4 Experiment Settings
### Evaluation of Conformal Prediction
#### 4.4.3 Effectiveness of Confident Predictions  
When the CP method returns a single-label prediction set \(\big{|}\mathcal{C}(\mathbf{x}_{i})\big{|}=1\), we say the prediction is confident. While Section 4.2 defines global metrics such as Precision, Recall, and MCC for the entire dataset, it is also insightful to evaluate these metrics exclusively on the subset of instances for which the prediction is single-labeled. This subset-specific view reveals how well the method actually performs when it chooses to be certain. In addition, we track Specificity and the Minority Class Ratio to understand the nature of confident decisions.  
Restricting Metrics to Confident SubsetLet  
\[\mathcal{D}_{\text{conf}}=\big{\{}\left(\mathbf{x}_{i},y_{i}\right)\in\mathcal{D }_{\text{test}}\;\big{|}\;\big{|}\mathcal{C}(\mathbf{x}_{i})\big{|}=1\big{\}} \tag{38}\]  
be the confident subset of the test data, i.e., all test points for which the conformal set is a single label \(\{0\}\) or \(\{1\}\). For each instance in \(\mathcal{D}_{\text{conf}}\), define the predicted label \(\hat{y}_{i}\equiv\mathcal{C}(\mathbf{x}_{i})\). We can then form a confusion matrix \(\text{TP}_{\text{conf}}\), \(\text{FP}_{\text{conf}}\), \(\text{TN}_{\text{conf}}\), \(\text{FN}_{\text{conf}}\) restricted to \(\mathcal{D}_{\text{conf}}\) and calculate:  
\[\text{Precision}_{\text{conf}} =\frac{\text{TP}_{\text{conf}}}{\text{TP}_{\text{conf}}+\text{ FP}_{\text{conf}}}, \tag{39}\] \[\text{Recall}_{\text{conf}} =\frac{\text{TP}_{\text{conf}}}{\text{TP}_{\text{conf}}+\text{ FN}_{\text{conf}}},\] \[\text{Specificity}_{\text{conf}} =\frac{\text{TN}_{\text{conf}}}{\text{TN}_{\text{conf}}+\text{ FP}_{\text{conf}}},\]  
where the terms in the numerators and denominators denote true positives, false positives, etc., respectively, on \(\mathcal{D}_{\text{conf}}\).  
In an imbalanced dataset, it is also crucial to assess how frequently confident predictions are for the minority (positive) class. Define:  
\[\text{Minority Class Ratio }[\%]\;=100\times\frac{\sum\limits_{i=1}^{N} \mathbb{I}\Big{(}\big{|}\mathcal{C}(\mathbf{x}_{i})\big{|}=1\;\wedge\; \mathcal{C}(\mathbf{x}_{i})=\{1\}\Big{)}}{\sum\limits_{i=1}^{N} \mathbb{I}\Big{(}\big{|}\mathcal{C}(\mathbf{x}_{i})\big{|}=1\Big{)}}, \tag{40}\]  
where \(\{1\}\) designates the positive (minority) class, and \(\mathbb{I}(\cdot)\) is the indicator function. A high value of Minority Class Ratio means that, among all single-label sets, a substantial fraction are \(\{1\}\). Depending on the accompanying Precision and Recall values for these cases, this could indicate either strong confident detection of positives or an overestimation of risk leading to potential false positives.
# Extraction and Synthesis of Silicon Nanoparticles (SiNPs) from Sugarcane Bagsase Ash: A Mini-Review
## 3 Reduction of Silica into Silicon Nanoparticles
### Carbothermic Reduction of Silica to Silicon  
Carbothermic reduction is traditionally an industrial procedure used to manufacture-grade silicon dioxide in an electric arc furnace according to the simplified reaction Equation (19) below. The method involves high temperatures (2000 \({}^{\circ}\)C) and high-energy consumption, shown in Figure 6. The flow chart displays the processes involved to get high purity silica from precursor materials to silicon, using the synthetic reduction routes. Some other methods involve tetrafluoride shown in (20) similar to (c) and (b) decomposition of silane (22), however, they might not be economically viable [42; 43; 44].  
\[SiO_{2}(s)+2C\to Si(s)+2 CO(g) \tag{19}\]  
\[SiF_{4}(g)+4 Na(l)\to Si(s,powder)+4 NaF(s) \tag{20}\]  
\[SiH_{4}(g)\to Si(s,powder)+2H_{2}(s) \tag{21}\]  
The electric arc furnace requires about 1900 \({}^{\circ}\)C to melt sand, which translates to 50 kWh of energy for a kilogram of sand. In addition, for 60,000 tons of metallurgical-grade silicon expected to be produced annually, the required energy could be 30 TWh. Therefore, it is notably evident that the aforementioned process is energy-intensive, making it quite expensive as well as environmentally unsustainable.  
Hence, it is worth investigating other alternative sources for silicon, agricultural wastes (sugarcane bagasse). The carbothermic procedure involves waste product on waste, which is advantageous for this route, synthesized activated carbon from waste material. Recently, studies have reported the use of the microwave process for carbothermic reduction of silica to silicon [44; 45].
# Two-Layer Security Algorithms to Prevent Attacks on Data in Cyberspace
## 3 Proposed Algorithm
### Data Decryption Process  
To decrypt the _data_, the _ciphertext_ (C) and Vigenere _text key_ [\(F_{[i]}\)] are implemented on the _Vigenere cipher algorithm_, and alphabetical _Vigenere results_ [\(V_{[i]}\)] are obtained, as shown in Figure 4 and Algorithm (2). After that, each _Vigenere cipher index letter_ [\(V_{[n]}\)] is converted to its _equivalent ASCII_ [\(W_{[i]}\)]. The results of [\(W_{[i]}\)] are _XORed_ with the _static key_ [\(K_{[i]}\)]. First, the Vigenere key should be accurate to decrypt the data, and then the _static key_ can be implemented. A _static key_ cannot be used until the Vigenere key is not valid. The better way to secure data is for _both keys_ to perform different functionalities. The algorithm can never be optimized if different functions are performed repeatedly with the same key. To _XOR_ the _Vigenere ASCII result_ [\(W_{[i]}\)] and the _static key_ [\(K_{[i]}\)], formula \(P_{\oplus[i]}=[W_{i}]\oplus[K_{i}]\) is used. \"\(P_{\oplus[i]}\)\" is the _XOR results_ that are obtained from [\(W_{i}\)] and [\(K_{i}\)] results. The results of \(P_{\oplus[i]}\) are converted to decimals by making _8-bit pairs_, each _decimal value_ is converted to its _equivalent ASCII_, and the _plaintext_ is obtained.
# High-Precision Single-Photon Laser Time Transfer with Temperature Drift Post-Compensation
## 4 Results and Discussion
### Data Processing and Analysis
#### 4.1.3 Analysis of the Combined Data  
The above compensated data are combined to an entire experiment process as shown in Figure 8a. The propagation delay drift of 240 ps has been removed almost perfectly.  
Figure 6: Delay over temperatures after compensation in the temperature rising period. (The black dots are the compensated data, and the blue line is the fitting curve).  
Figure 7: TDEV before and after compensation in the temperature rising period.  
We also got the stabilities of the combined data with and without compensation as shown in Figure 8b. One can see from the TDEV diagram that the short-term stability gets worse in the averaging time between approximately 20 s and 1300 s; however, the long-term stability is much lower than approximately half an order of magnitude from the original value of 11.5 ps down to 2.5 ps for averaging time of 80,000 s.
# The Design of a Long-Distance Signal Transmission System in a Bistatic RCS Testing System
## 1 Introduction
The RCS testing system must ensure coherence between the transmitted and received signals. In monostatic radar systems, where the transmitter and receiver are co-located at the same base station, time and frequency synchronization is readily achievable through the use of a shared clock source, ensuring signal coherence. In bistatic radar systems, where the transmitter and receiver are spatially separated, each must use its own clock. The differences between these clocks \begin{table}
\begin{tabular}{c c c} \hline \hline
**Target** & **RCS (m2)** & **RCS (dBm2)** \\ \hline Insect & 0.00001 & \(-\)50 \\ Bird & 0.01 & \(-\)20 \\ Human & 1 & 0 \\ Bicycle & 2 & 3 \\ Automobile & 100 & 20 \\ Pickup truck & 200 & 23 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: RCS values of common objects [21]. make it difficult to coordinate transmission and reception. There are two common methods: One consists of equipping both bases with high-precision atomic clocks. The coherent population trapping (CPT) atomic clock, widely used in synchronization communications, has a frequency stability of approximately \(10^{-10}\), while the highest-precision rubidium atomic clocks can achieve a frequency stability of \(10^{-14}\)[24]. However, the production and maintenance costs of high-precision atomic clocks are extremely high. Another method consists of extracting a synchronization signal from one base and transmitting it to another base, thereby achieving synchronization between the two bases. The key to this method is ensuring that the synchronization signal maintains phase stability after long-distance transmission between the two bases. This article presents a signal transmission system designed for a bistatic RCS testing system to address the issue of time synchronization. As the testing system is an electromagnetic measurement system and the distance between the two radar stations is over 300 m, using wireless electrical signal transmission could interfere with the scattered waves, affecting the test results. On the other hand, using wired electrical signal transmission over such distances would lead to significant attenuation. Therefore, our design adopts an optoelectronic conversion approach. Before transmission, the reference signal is converted into an optical signal, transmitted via optical fiber to the other base station, and then demodulated back into an electrical signal. The optoelectronic conversion system is linear and does not disrupt the coherence of the entire testing system. The linearity of this system is a key indicator in determining whether RCS measurements can be performed. In the following Section 3, the phase stability of the system is specifically tested, and in Section 4, the negative impacts that system nonlinearity may have on the overall RCS measurements are analyzed.