esa-sceva/satcom-chunk-collection huggingface dataset:
# TAEO-A Thermal Aware & Energy Optimized Routing Protocol for Wireless Body Area Networks
## 4 Proposed Methodology-TAEO
### Initialization  
In this phase, each node broadcasts a packet containing the coordinates to its location, energy level, heat value, and the node identifier. In this way, all of the nodes are updated with the location of their neighbors and related information. Sink then broadcast a packet and disseminates its location to all of the nodes on the body. The temperature threshold is a value where there is a chance of tissue damage. This becomes two-fold when the temperature value exceeds from the threshold value. The most important aspect in WBAN is to avoid tissue damage as the heat that is generated by the node's circuitry and antenna could cause damage to the human tissue [25]. The fact that the human body has a thermoregulatory mechanism to balance the heat around the body does not mitigate the  
Figure 2: Operational Flow Diagram.  
issue of tissue damage when the heat received rate is larger than the thermoregulatory mechanism rate [25]. This issue can be resolved by avoiding those implantable nodes in the routing path that have been heavily utilized and may cause a risk of tissue damage. A metric, which is called Specific Absorption Rate (SAR), can be used to measure the rate at which energy is absorbed by tissues when exposed to an electromagnetic field. Experiments show that exposure to an SAR of 8 W/Kg in any gram of tissue in the head or torso for 15 min may have a significant risk of tissue damage [26]. Even with more modest heating, organs that are especially sensitive to any temperature increase due to a lack of blood flow to them are prone to thermal damage (e.g., lens cataracts [27]). Researchers have also expressed concern regarding the heating of the eye by examining the SAR and the temperature of the eye when exposed to a Wireless LAN [28] or Infrared [29] radiation. In [30], a detailed description about the thermal effects of bioimplants has been presented.  
When considering the above discussion, the threshold of the parameter Temp used in Figure 2 is set according to the application requirement where the system is going to be used. It would be different when the sensors are places in the head instead of any other part of the body.
# An Analytical Method for Gas Flow Measurement Using Conservative Chemical Elements
## 2 Materials and Methods
### Measurement of Gas Flow Rate  
We measured the gas flow rate using two approaches for all of the experiments in this study. Method I was a mechanical method in which an MFM was calibrated with nitrogen (N\({}_{2}\)). In the gas mixtures, for which we knew the gas composition and the mixing ratio, we calculated the flow rate considering the gas conversion factor. In contrast, in the chemical activation, we did not consider the gas conversion factor because it was difficult to predict the changes in gas composition. In this study, we calculated flow rates (Method II) using the conservative element (N\({}_{2}\)). Elements such as He, N\({}_{2}\), and Ar, in a gaseous phase, are non-reactive gases that do not participate in chemical reactions under a given set of conditions [4; 5]. For this reason, we can assume that changes in the concentration of these chemical species in the reaction are only affected by physical mixing. Therefore, if a constant flow of inert gas is continuously injected during an entire reaction period, we can estimate the gas flowrate from the volume fraction (VF) of these inert gases, as shown in Equations (1) and (2).  
\[\text{VF}_{\text{\_CE}}=\text{CON}_{\text{\_CE}\left(\text{t}=\text{i}\right)}/ \text{CON}_{\text{\_CE}\left(\text{t}=\text{0}\right)} \tag{1}\] \[\text{Total gas flow rate}=\text{FI}_{\text{\_CE}}/\text{VF}_{\text{ \_CE}} \tag{2}\]  
where VF\({}_{\text{\_CE}}\) = volume fraction of a conservative element,  
\(\text{CON}_{\text{\_CE}}\) = concentration of a conservative element,  
\(\text{FI}_{\text{\_CE}}\) = injected constant gas flow rate of a conservative element from the beginning,  
t = elapsed time (min),  
(0: before the reaction starts, i: time elapsed after reaction).
## 2 Materials and Methods
### Y-Maze  
The Y-maze was performed during weeks 4 and 10 of Cr(VI) exposure. The Y-maze consisted of three radial arms angled in the shape of a 'Y', fully enclosed by walls. Each arm was 51 cm \(\times\) 15 cm \(\times\) 43 cm (L \(\times\) W \(\times\) H); arms were connected by an equilateral triangle with 15 cm sides. Rats were individually placed in the middle of the Y-maze and allowed to freely explore for 8 min while being recorded from an aerial view. Spatial memory was assessed as changes in non-alternating arm entry, while general activity was assessed as the frequency of arm entries [48]. The maze was cleaned with 70% ethanol between each recording session. Behavior was scored manually by tracking the sequence of arm entries to determine the number of alternations or non-alternations. An alternation was counted as all three arms entered in a sequence, whereas a non-alternation was counted as an arm entered more than once in a sequence of three entries. An entry was counted when all four paws crossed from the center triangle into an arm. Rats inherently explore novel areas; thus, increased non-alternations capitulate impaired spatial memory. The percentage of non-alternations was calculated using the following equation:  
\[(\#NonAlternations/(\#Alternations+\#NonAlternations))\times 100\]Arm entries per minute were scored manually to assess activity. An arm entry was counted when all limbs left the center triangle and entered the arm. Increased arm entries per minute were interpreted as hyperactivity, while decreased arm entry frequency was interpreted as hypoactivity. Recorded trials were blinded prior to manual scoring.
# Investigation of Energy Cost of Data Compression Algorithms in WSN for IoT Applications
###### Abstract  
The exponential growth in remote sensing, coupled with advancements in integrated circuits (IC) design and fabrication technology for communication, has prompted the progress of Wireless Sensor Networks (WSN). WSN comprises of sensor nodes and hubs fit for detecting, processing, and communicating remotely. Sensor nodes have limited resources such as memory, energy and computation capabilities restricting their ability to process large volume of data that is generated. Compressing the data before transmission will help alleviate the problem. Many data compression methods have been proposed but mainly for image processing and a vast majority of them are not pertinent on sensor nodes because of memory impediment, energy utilization and handling speed. To overcome this issue, authors in this research have chosen Run Length Encoding (RLE) and Adaptive Huffman Encoding (AHE) data compression techniques as they can be executed on sensor nodes. Both RLE and AHE are capable of balancing compression ratio and energy utilization. In this paper, a hybrid method comprising RLE and AHE, named as H-RLEAHE, is proposed and further investigated for sensor nodes. In order to verify the efficacy of the data compression algorithms, simulations were run, and the results compared with the compression techniques employing RLE, AHE, H-RLEAHE, and without the use of any compression approach for five distinct scenarios. The results demonstrate the RLE's efficiency, as it surpasses alternative data compression methods in terms of energy efficiency, network speed, packet delivery rate, and residual energy throughout all iterations.  
data compression; RLE; adaptive Huffman encoding; H-RLEAHE; IoT 202222226788180390/22/19/685
# Evaluation of Forward Models for GNSS Radio Occultation Data Processing and Assimilation
## 3 Results
### Experiment 2: Evaluation of the Error of the FM on the Fixed Model Level
The FM is the exp algorithm, together with (**a–c**) log-cubic or (**d–f**) log-linear. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Statistics** & **MSL Height** & **Order** & **EC 4 Dvar** & **EC 4 Dvar** & **Msl Height** & **FNL(31)** & **ERA5(37)** \\  & **(km)** & **of Magnitude** & **(cubic)** & **(lin)** & **(km)** & **(cubic)** & **(cubic)** \\ \hline Relative & 0–35 & & \(\pm 0.5\%\) & \(\pm 1\%\) & 0–30 & \(\pm 2.5\%\) & 3\% \\ difference & 35–58 & **\%** & \(\pm 4\%\) & \(\pm 4\%\) & 30–40 & \(\pm 5\%\) & 5\% \\ (RD) & 58–80 & & \(\pm 1.8\%\) & \(\pm 2\%\) & 40–50 & \(\pm 15\%\) & 10\% \\ \hline \multirow{4}{*}{Difference} & 0–10 & \(1\times 10^{-5}\) & \(\pm 4\times 10^{-5}\) & \(\pm 6\times 10^{-5}\) & 0–11 & \(\pm 4\times 10^{-4}\) & \(\pm 2\times 10^{-4}\) \\  & 10–35 & \(1\times 10^{-6}\) & \(\pm 2\times 10^{-6}\) & \(\pm 8\times 10^{-6}\) & 11–22 & \(\pm 4\times 10^{-5}\) & \(\pm 5\times 10^{-5}\) \\ \cline{1-1}  & 35–50 & \(1\times 10^{-6}\) & \(\pm 3\times 10^{-6}\) & \(\pm 4\times 10^{-6}\) & 22–40 & \(\pm 2\times 10^{-5}\) & \(\pm 2\times 10^{-5}\) \\ \cline{1-1}  & 50–60 & \(1\times 10^{-7}\) & \(\pm 2\times 10^{-7}\) & \(\pm 4\times 10^{-7}\) & 40–46 & \(\pm 2\times 10^{-6}\) & \(\pm 2\times 10^{-6}\) \\ \cline{1-1}  & 60–80 & \(1\times 10^{-7}\) & \(\pm 4\times 10^{-8}\) & \(\pm 6\times 10^{-8}\) & 46–50 & \(\pm 6\times 10^{-6}\) & \(\pm 6\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: RD and difference for forward modeled analyses in MLS (mean sea level, km). Figure 7: Under various model levels, the difference (**a**–**c**) and RD (**d**–**f**) of FM based on the exp. algorithm and log-cubic interpolation. The RD for EC4 Dvar below 35 km is less than \(\pm 0.5\%\), whereas RDs for FNL and ERA5 below 35 km are less than\(\pm 5\%\). Above 35 km, the RD for EC4 Dvar is less than \(\pm 5\%\), while RDs for FNL and ERA5 are up to 10%.
## 4 System Model
### Node Selection in the Optimal Route Based on \(Q\)-Learning Algorithms:
The flowchart of MRLAM routing scheme shown in Figure 3 describes the process of selection of optimal route from source to destination based on Q-learning algorithms. The MRLAM routing scheme selects the optimal route applying decision policy-based Q-learning algorithms, which can effectively reduce the redundant information in the network topology and enhance overall network performance. The route selection based on mobility, residual energy, and link quality with the Q-Learning algorithm is described below: \begin{tabular}{|p{284.5 pt}|p{170.7 pt}|} \hline \multicolumn{3}{|c|}{**Q-Learning Algorithms for Route Selection**} \\ \hline
1. & At every edge and node between source and destination node \\ \hline
2. & **Initialize**\(Q_{i}(s_{t}^{i},a_{t}^{i})\)\(\forall(s\in S,a\in A)\) \\ \hline
3. & Choose action \(a_{t}^{i}\) from the state \(s_{t}^{i}\) using best policy derived \(Q_{t+1}^{i}\) \\ \hline
4. & **Q-value** at time as: \\ \hline
5. & Based on mobility, residual energy and link quality new reward function \\ \hline  & \(R_{t+1}^{i}=w_{1}\times\text{Mobility}+w_{2}\times\text{R}+w_{3}\times\text{ Link Quality}\) \\ \hline
6. & **Update Q-value** with new reward function as: \\ \hline  & \(Q_{t+1}^{i}(s_{t}^{i},a_{t}^{i})\leftarrow(1-\alpha)\times Q_{t}^{i}(s_{t}^{i},a _{t}^{i})+\alpha\Big{[}R_{t+1}^{i}(s_{t+1}^{i})+\gamma\times\max_{a\in A}Q_{t} ^{i}(s_{t+1}^{i},a_{t+1}^{i})\Big{]}\) \\ \hline
7. & Select the optimal route with update Q-value \\ \hline \end{tabular}
## 2 Materials and Methods
### Characterization Methods  
Characterization powder X-ray diffraction (XRD) for the crystal structure of the products was measured in an X'Pert PRO Multi-purpose X-ray Diffraction System (Bruker D8 advance, Karlsruhe, Germany) using Cu K radiation (\(\lambda\) = 0.1541 nm). Fourier transforms infrared (FTIR) spectra were obtained using a Nicolet 6700 FTIR Microscope spectrometer (Thermo Fisher, Waltham, MA, USA) to identify the functional groups. The X-ray photoelectron spectroscopy (XPS) data were recorded on an ESCALAB 250 Xi X-ray photoelectron spectrometer (Thermo Fisher, Waltham, MA, USA). The scanning electron microscopy (SEM) was performed on a scanning electron microscope (SEM, FEI, Hillsboro, OR, USA). The high-resolution transmission electron microscopy (HRTEM) observations were carried out using a Tecnai G2 F30. High-resolution Transmission electron Microscope (HRTEM, FEI, Hillsboro, OR, USA) equipped with a high angle annular field (HADDF) detector. Nitrogen adsorption-desorption isotherm was determined by the JW-BK132F instrument (JWGB, Beijing, China).
# Enhanced Scheduling of AI Applications in Multi-Tenant Cloud Using Genetic Optimizations
## 4 Performance Evaluation  
In this section, we perform simulation experiments to validate the effectiveness of the proposed scheduling in terms of GPU utilization within clusters. For comparison, we simulate other scheduling algorithms utilized for machine learning tasks, along with the proposed scheduling. The results suggest that the proposed algorithm can function as a proficient scheduling strategy in cluster settings where GPUs are shared, leading to enhanced GPU utilization without performance penalties.  
We implement a scheduling simulation framework to evaluate the performance of machine learning scheduling. The framework is based on Simpy, a process-based discrete-event simulation framework, and utilizes PyGAD 3.3.1, an open-source Python library for genetic algorithms [31]. This paper constructs the input data for scheduling based on ML workload traces collected over two months from the Alibaba PAI cluster. As shown in Table 1, this cluster includes over 1800 nodes and more than 6000 GPUs, and the machines are heterogeneous in terms of hardware and resource configuration. The workloads submitted to the cluster are a mix of training and inference tasks and include a variety of machine learning algorithms such as CNN, RNN, transformer-based language models, and reinforcement learning. A total of 1200 tasks, comprising 7500 task instances, were submitted, and the trace data include submission time, cluster usage time, and requests for CPU, memory, and GPU quantities per instance. This paper uses a sampling of the entire workload trace to generate input data for scheduling.  
As jobs are submitted at different times in real scenarios, our scheduling should be updated periodically, implying that the genetic algorithms are run using jobs submitted during the current period, as well as those remaining in the queue from the previous period that have not yet been dispatched. It is important to note that this periodic scheduling does not affect jobs that have already been dispatched and are currently being executed. In our performance evaluations, which are conducted through simulations that incorporate complete job histories, we recognize the submission times of all jobs from the beginning of our scheduling process. Nevertheless, we perform our scheduling periodically, assuming knowledge of only those job submission times that occur up to the current simulation point.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**System**} & **Number of CPUs** & **Memory** & **Number of GPUs** & **GPU Type** & **Number of Nodes** \\ \hline PAI & 64 & 512 & 2 & P100 & 798 \\  & 96 & 512 & 2 & T4 & 497 \\  & 96 & 512 & 8 & Misc & 280 \\  & 96 & 384 & 8 & V100M32 & 135 \\  & 96 & 512/384 & 8 & V100 & 104 \\  & 96 & 512 & 0 & N/A & 83 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The experimental configuration of the proposed scheduling.
## 3 Variables and Model
### Model
\begin{array}{c}\Delta\text{CO}_{2,t}=\text{b}_{0}+\sum_{i=0}^{n1}\text{b}_{ 1i}\Delta\text{CO}_{2,t-i}+\sum_{i=0}^{n2}\text{b}_{2i}\Delta\text{pt}_{t-i}+ \sum_{i=0}^{n3}\text{b}_{3i}\Delta\text{ee}_{t-i}+\\ \sum_{i=0}^{n4}\text{b}_{4i}\Delta\text{eg}_{t-i}+\sum_{i=0}^{n5}\text{b}_{5i} \Delta\text{wt}_{t-i}+\sum_{i=0}^{n6}\text{b}_{6i}\Delta\text{int}_{i-i}+\sum_ {i=0}^{n7}\text{b}_{7i}\Delta\text{fd}\text{i}_{t-i}+\\ \sum_{i=0}^{n8}\text{b}_{8i}\Delta\text{gt}_{t-i}+\text{b}_{5}\text{CO}_{2,t-1 }+\text{b}_{10}\text{pt}_{t-1}+\text{b}_{11}\text{ee}_{t-1}+\text{b}_{12}\text{ ge}_{t-1}+\text{b}_{13}\text{wt}_{t-1}+\\ \text{b}_{14i}\text{in}_{t-1}+\text{b}_{15}\text{fd}\text{i}_{t-1}+\text{b}_{16 }\text{gt}_{t-1}+\text{F}_{1}\text{sin}\left(\frac{2\pi\text{kt}}{\text{T}} \right)+\text{F}_{2}\text{cos}\left(\frac{2\pi\text{kt}}{\text{T}}\right)+\text{ ec}_{t-1}+\text{e}_{t}.\end{array}  \tag{3}\]  
Equation (3) introduces two key components of the Fourier transformation: \(\text{F}_{1}\) and \(\text{F}_{2}\), which represent the amplitude and displacement, respectively. In this context, the term \(\pi\) is the mathematical constant (approximately 3.14), while k denotes the frequency parameter associated with the Fourier series. The variable t captures the time trend, and T refers to the total sample size. The Fourier transformation enhances the model by allowing for smooth, periodic shifts in the data, which can capture underlying cyclical patterns and structural changes without relying on predefined breakpoints. This approach is particularly advantageous in econometric models where abrupt shifts in the underlying data-generating process might bias traditional estimates. The inclusion of Fourier terms in ARDL models has been shown to improve model flexibility and fit by capturing non-linearities that might otherwise be overlooked ([PERSON] et al. [62]; [PERSON] et al. [63]; [PERSON] et al. [64]). Moreover, this method allows the identification of structural breaks without the need for prior assumptions about their timing, thus offering a more nuanced approach to understanding long-run relationships in the presence of such breaks.
# Biochar for Wastewater Treatment--Conversion Technologies and Applications
## 5 Environmental Application of Biochar for Wastewater Treatment
### As a Support Media during Anaerobic Digestion
The alkalinity of biochar is depending on the biomass source as well as on the carbonization temperature, with the alkalinity of biochar increases as the pyrolysis temperature raised [38; 171]. The immobilization of biomass on a packing material that provides a very large surface area for microbial growth has been proved as an advantageous alternative, since it allows developing a large bacterial biomass that can be maintained within the reactors for a very long operation time, facilitating electron transfer between interspecies and reducing the distance between syntrophic bacteria and methanogens [172; 173; 174; 175; 176]. This can be beneficial to reduce the process start-up time, to assure better stability and higher tolerance toward high loading rates, and to allow for microorganisms to recover their performance very quickly after a period of starvation [177]. Packing materials such as zeolite, clay, activated carbon, and other plastic materials have been used to support microbial attachment and growth [178; 179; 180; 174; 175; 176; 177; 178; 179; 181]. The application of biochar for cell immobilization is, however, not as extensive as most other adsorbents. In a study performed by [PERSON] et al. [182], the authors reported that biochar promoted the methanogenic biofilm formation, which enhanced the microbial activity, volatile fatty acids degradation and CH\({}_{4}\) production. [PERSON] et al. [164] observed the colonization of _Methanosarina_ on biochar material during anaerobic digestion of glucose. The authors showed that the use of biochar leads to an increase in biogas production by about 86% with a simultaneous enhancement of the degradation of intermediate acids as compared to the non-biochar study. The different studies performed so far, showed that the addition of biochar increases microbial metabolism and growth and provides favorable support for microorganisms [165; 167]. However, no specific description on the special distribution of microorganisms on the biochar surface and the relationship between biochar structure and the amount of the immobilized microorganisms is provided.
## 3 Data Source and Data Preprocessing
### Data Hierarchical Gridding  
Hierarchical gridding of the data is applied to avoid the shock effect of the model in the horizontal and vertical directions caused by the uneven data distributions. In the horizontal direction, the grid resolution of data is set to 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\). In the vertical direction, the layered interval of data is set to 10 km. A layer of insufficient data is merged into an adjacent layer. For the data distributed in the same grid unit, the data nearest the average value of the grid is selected as the magnetic anomaly value of the grid to ensure the equilibrium of the data in the horizontal and vertical directions.
# Biomotor Abilities for Law Enforcement Officer Readiness
## 3 Results  
A total of 2420 studies were identified through the initial search of three databases. After the removal of duplicates and review by title and abstract, full-text versions of 57 studies were collated for review. These studies were then evaluated against the inclusion and exclusion criteria, after which 17 studies remained for critical review (Table 2). A summary of screening, selection processes, and literature search results can be found in the PRISMA flow diagram (Figure 1) [13]. All studies included LEOs. Twelve studies examined male and female participants [15, 19, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], whereas the remaining five included only male participants [16, 17, 18, 20, 21]. Table 4 displays the frequency of significant biomotor ability correlates to occupational tasks among these studies. The frequency calculations are based on the percentage of studies demonstrating significant correlations, providing a quantitative overview of how biomotor abilities impact occupational tasks across diverse LEO populations. The following classifications were used to assess the strength of the correlations: trivial (r = 0-0.1), weak (r = 0.10-0.39), moderate (r = 0.40-0.69), strong (r = 0.70-0.89), and very strong (r = 0.90-1.0) [32]. \begin{table}
\begin{tabular}{l l c c} \hline \hline
**Biomotor Ability** & **Occupational Task** & **Frequency (\%)** & **References** \\ \hline Aerobic capacity—Absolute & \(\bullet\) 99-yard Obstacle Course & 18\% & [16, 18, 19, 30, 31] \\ (L/min) & \(\bullet\) 500-yard Run & & \\ \hline Aerobic capacity & \(\bullet\) 99-yard Obstacle Course & 29\% & [16, 18, 20, 28, 30] \\ (mL/kg/min) & \(\bullet\) 500-yard Run & & \\ \hline Anaerobic capacity & \(\bullet\) 75-foot Pursuit Run & 12\% & [17, 18, 19, 30] \\ \hline Muscular endurance & \(\bullet\) 99-yard Obstacle Course & 65\% & [15, 16, 17, 18, 19, 31] \\ \hline Muscular strength & \(\bullet\) Body Drag & & \\ \(\bullet\) Solid Wall Climb & 41\% & [16, 17, 18, 19, 22, 23, 28, 29, 31] \\ \(\bullet\) Chain Link Fence & & & \\ \hline Power & \(\bullet\) Body Drag & & \\ \(\bullet\) Solid Wall Climb & 47\% & [15, 16, 17, 18, 22, 23, 27, 28, 30, 31] \\ \(\bullet\) Chain Link Fence & & & \\ \hline Agility & \(\bullet\) 99-yard Obstacle Course & & \\ \(\bullet\) 500-yard Run & & & \\ \(\bullet\) 75-foot Pursuit Run & & & \\ \(\bullet\) Body Drag & 41\% & [15, 16, 18, 19, 22, 28, 29, 31] \\ \(\bullet\) Solid Wall Climb & & & \\ \(\bullet\) Chain Link Fence & & & \\ \hline Flexibility & \(\bullet\) Solid Wall Climb & 18\% & [15, 16, 19, 22, 29, 31] \\ \(\bullet\) Chain Link Fence & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Frequency of significant correlations between each biomotor ability and occupational tasks identified in the selected literature.