esa-sceva/satcom-chunk-collection huggingface dataset:
# Growing Kratky Basil in Trombe Wall Cavity: Energy Performance
## 4 Discussion
### Suggestions and Opportunities for Further Research  
As an exploratory study, several limitations and areas for improvement were identified, laying the groundwork for further research. Key recommendations include the following topics:  
* Plant variety: Experimenting with a broader range of plant species to evaluate their adaptability and growth performance within the Trombe wall environment.
* Water mass: Increasing the thermal mass by introducing additional water reservoirs (e.g., more bottles placed behind the plants or by the back wall) to optimize temperature regulation.
* Glazing protection: Testing shading solutions for summer and insulating covers for winter and nighttime to extend the cultivation period and improve thermal stability.
* Perennial cultivation: Developing strategies to support year-round cultivation, allowing perennial plants to thrive permanently within the Trombe wall cavity.
* Simulations: Utilizing computational models to analyze the interplay of factors such as thermal behavior, humidity, and plant growth under varying climatic conditions, geometries, and operational configurations.
* Climatic and geographic variability: Testing the system's viability in diverse climates and locations to assess its adaptability and performance.
* Alternative designs: Investigating the use of alternative hydroponic systems, plant arrangements, and material configurations to enhance the efficiency and sustainability of the system.
* Hybrid systems: Combining the green Trombe wall concept with other technologies such as photovoltaics, phase-change materials, and solar panels to maximize their energy and environmental benefits.
# An Efficient Model-Based Clustering via Joint Multiple Sink Placement for WSNs
## 5 Simulation and Evaluation
### Simulation Results  
Table 5 illustrates the fundamental differences relative to the number of hops and clusters. The IAC algorithm is compared to LEACH, HEMP, and DP clustering to obtain the cluster heads. Our technique shows fewer nodes and clusters with multiple hops than the compared algorithms. Moreover, the maximum number of hops required to reach CH is 3. However, LEACH's, HEMP's, and DP's clustering are 5, 4, and 4, respectively, since the IAC clustering algorithm is more effective than other clustering techniques on a large scale.
###### Abstract  
A laboratory investigation was carried out to determine the optimum soil to fly ash mix ratio to enhance the engineering properties of clayey sand that can potentially be used as a road subgrade. Grain size distribution and Afterberg limits tests were conducted to classify the soil and to study the effects of the fly ash on the soil plasticity. The Proctor test was conducted to determine the optimum moisture content and maximum dry density of soil-fly-ash mixtures with arbitrarily selected 0%, 40%, 50%, and 60% fly ash content. A higher percentage was selected to find the highest optimum fly ash content to maximize the beneficial use. Unconfined compression and consolidation tests were conducted with air-dry arbitrarily selected curing periods of 0, 2, 8, and 28 days to determine the strength and to predict the settlement and the volume change behavior. It can be concluded from the trend analysis that a fly ash content range of 32-50% appeared to be optimum that is expected to perform better as subgrade materials for a curing period range of 16-19 days. However, experimental data showed a fly ash content of 50% was the optimum for a curing period of 8 days. The settlement and the volume change behavior improved at least 44% with increased fly ash content.  
Keywords:fly ash; engineering properties of soil; soil stabilization; optimization of fly ash content; optimum moisture content (OMC); maximum dry density (MDD); unconfined compression strength (UCS) +
Footnote †: journal: Applied Sciences
# Video Human Action Recognition Based on Motion-Tempo Learning and Feedback Attention
## 3 The Proposed Critical Action Information Extraction Network  
In this section, we will describe the proposed SMEN. As depicted in Figure 2, SMEN uses ResNet50 as its backbone and is composed of two main components: the Multi-Granularity Adaptive Fusion Module (MgAFM) and the Feedback Attention-Guided Module (FAGM). Initially, the \(T\) visual frames of the action videos are processed via convolution operations and encoded into feature maps. To capture motion features of sub-actions with varying tempos, the designed MgAFM is introduced following the layer Res3. The temporal dynamics are explored across multiple temporal spans, incorporating various temporal granularities, and then are fused with RGB appearance information adaptively. To identify the most important contextually relevant feature maps, a feedback attention module (FAGM) is designed for video representation learning along temporal, channel, and spatial dimensions.  
Figure 2: The architecture of our SMEN. It takes ResNet50 [31] as its backbone and primarily consists of MgAFM and FAGM modules. The SMEN takes \(T\) visual frames of the action videos as input and transforms them into frame-wise appearance features. In MgAFM, the RGB frames are utilized to capture sub-action motion dynamics with fast and slow tempos. In FAGM, multi-level features are combined as input and then are passed through attention modules along the temporal, channel, and spatial dimensions to generate attention weights. These weights guide low-level features to focus on essential sub-action information.
## 3 Results
### Overview of Mars ISRU Technologies
The Sabatier reaction will be important when hydrogen becomes available, converting H\({}_{2}\)O and CO\({}_{2}\) to CH\({}_{4}\) and O\({}_{2}\)[11; 13; 14]. The Sabatier process was used in end-to-end lab demonstrations [15; 16]. The CO is also used in the Fischer-Tropsch synthesis to produce hydrocarbons and methanol. The produced CH\({}_{4}\), CO, and O\({}_{2}\) can be used in a propellant mixture in rocket vehicles [15]. The N\({}_{2}\) can be converted to NH\({}_{3}\) by the Haber-Bosch synthesis [12]. These processes must be designed to work under Martian environmental and gravity conditions. An important aspect, sometimes overlooked, is that the above reactions produce mixtures that must be separated using processes such as distillation and membrane separation. Also, processing of Martian regolith is challenging but might also provide Fe, Al, and Si in future generations of landers. An early prototype of an integrated Mars atmosphere and soil processing system was developed during the MARCO POLO project [15]. Table 2 provides a summary of potential Mars ISRU technologies. In addition to all these chemical processes, other ISRU processes have been proposed, such as regolith usage for building and radiation shielding, crop production, etc. In this paper, we are primarily concerned with the near-term ISRU processing of atmosphere to produce oxygen, which we think would be the only practical application for the first human landing. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Process** & **Description** & **Conditions** & **Common Materials** \\ \hline \multicolumn{4}{c}{Reactions} \\ \hline RWGS & CO\({}_{2}+\)H\({}_{2}\) \(\rightarrow\) CO + H\({}_{2}\)O & 700–900 \({}^{\circ}\)C & * Fe\({}_{2}\)O\({}_{3}\)@ Al\({}_{2}\)O\({}_{3}\) \\  & 1 bar & 1 bar & \\ Sabatier & CO\({}_{2}+\)4H\({}_{2}\) \(\rightarrow\) CH\({}_{4}\) + H\({}_{2}\)O & 200–500 \({}^{\circ}\)C & * Ni@Al\({}_{2}\)O\({}_{3}\) \\ Fischer–Tropsch & \(\begin{array}{c}\text{nCO}+(2n+1)\text{H}_{2}\) \(\rightarrow\) C\({}_{n}\text{H}_{2n+2}+\text{nH}_{2}\text{O}\) \\ \text{nCO}+2\text{nH}_{2}\) \(\rightarrow\) C\({}_{n}\text{H}_{2n}+\text{nH}_{2}\text{O}\) & 20–40 bar & * Fe\({}_{3}\)O\({}_{4}\)@ Al\({}_{2}\)O\({}_{3}\) \\ Methanol & CO\({}+2\text{H}_{2}\)\(\rightarrow\) CH\({}_{3}\)OH & 200–300 \({}^{\circ}\)C & * CuO@Al\({}_{2}\)O
## 2 Results and Discussion
### Optimization of the Solid-Phase Extraction Procedure
To ensure all the target analytes were eluted into the F1 fraction, a second elution with an additional 15 mL of acetonitrile (F2 fraction) was evaluated for the presence of target analytes. When using Oasis MAX SPE sorbent, the elution process also differed with 5 v% NH\({}_{4}\)OH in H\({}_{2}\)O used in the wash step and 2 v% formic acid in acetonitrile, used for the elution following a similar procedure to the previously reportedmethod [44]. Poor recoveries were observed with Oasis MAX and could be partially attributed to the presence of additives (formic acid) used in the elution solvent, which could significantly reduce the MS sensitivity even if only traces of formic acid were present in the injected sample. Oasis HLB provided good recoveries (>80%) for all target analytes but required large elution volumes as analytes were still present in the F2 fraction (see Figure 2), and this would subsequently increase the time required for the drying step. With C18, >80% recoveries of the target analytes were observed in the first fraction, F1. The reproducibility of recoveries for target analytes (obtained with F1 and F2) when C18 SPE sorbent was used was also better than Oasis HLB, as shown in Figure 2. Other studies have also chosen C18 (500-1000 mg) for the analysis of other EDCs not selected in this study due to better reproducibility [45; 46]. Figure 1: Elution profile of BPA, 4 OP and 4 NP for SPE with C18 (1000 mg/6 mL) sorbent and total volume of 20 mL of elution solvents. Elution solvent: (**A**), methanol; (**B**), 50/50 \(\forall\)\(\forall\)\(\forall\)\(\forall\) methanol/acetonitrile; and (**C**), acetonitrile. Figure 2: Average recoveries of selected target analytes BPA, 4 OP, 4 NP, E2, and EE on Oasis HLB and MAX at pH = 3, and C18 at pH = 7. F1, 1 st eluted fraction with volume of 15 mL acetonitrile; F2, 2 nd eluted fraction with volume of 15 mL acetonitrile. The error bars represent the relative standard deviation determined from triplicate samples. Recovery tests were completed for the full list of target analytes in this study at three concentrations (20, 100, and 500 ng/L). To account for a small fraction of target analytes observed in the F2 fraction when the C18 sorbent was used (see Figure 2), the elution volume was increased from 15.0 to 15.2 mL. The recoveries of the target analytes, as shown in Table 1, were in the desired range of 85-110% except at the low concentration (20 ng /L) for estriol (E3), 4 NP, and (PRNL), but still greater than 80% and acceptable. The percent relative standard deviation was less than 15%.
## 3 Main Results
### The Combined Controller for Speed Regulation  
To obtain better speed regulation performance, a combined controller is designed for PMSM based on the system function (9) in this subsection.  
First, the sliding mode surface is introduced as  
\[s=cz_{1}+z_{2} \tag{19}\]  
where \(c\) is the sliding parameter. Then, one can get that the derivative of Equation (19) is shown in Equation (20).  
\[\dot{s}=c\dot{z}_{1}+\dot{z}_{2}=cz_{2}-Du \tag{20}\]  
The exponential reaching law is employed to ensure the dynamic performance, thus it can be obtained that  
\[u=\frac{1}{D}[cz_{2}+\varepsilon\text{sgn}(s)+qs] \tag{21}\]  
in which, \(\varepsilon\) and \(q\) are corresponding parameters. Hence, the current at \(q\) axis can be rewritten as  
\[i_{q}=\frac{1}{D}\int_{0}^{t}{[cz_{2}+\varepsilon\text{sgn}(s)+qs]d\tau} \tag{22}\]  
Note that, the integral term in Equation (22) is used to reduce the chattering of sliding mode surface and eliminate system steady state error. However, the response ability of the system will be decreased.  
In this paper, PD control and sliding control are combined to get better dynamic performance. That is, the control law in (22) is presented as  
\[i_{q}=\frac{1}{D}\!\int_{0}^{t}\left[cz_{2}+\varepsilon\text{sgn}(s)+qs\right] \!d\tau+K_{p}z_{1}+K_{D}z_{2} \tag{23}\]  
in which, \(K_{p}\) and \(K_{D}\) are gain parameters. Specially, one can set that \(K_{D}/K_{p}=Nc\). It should be pointed out that the control law in Equation (23) is equal to the sliding mode control with general reaching technique.  
\[\dot{s}=-\varepsilon\text{sgn}(s)-qs-N\dot{s} \tag{24}\]  
In other words, \((1+N)\dot{s}=-\varepsilon\text{sgn}(s)-qs\), and the value of \(K\) can be adjusted to improve the response speed.  
**Theorem 2**.: _Based on system (9) and reaching technique (24), it can be obtained that the designed control law can get good performance if and only if the following principles holds_  
\[\left\{\begin{array}{l}q+\varepsilon>0\\ N>-1\end{array}\right.\;\;\;\text{or}\left\{\begin{array}{l}q+\varepsilon<0 \\ N<-1\end{array}\right. \tag{25}\]  
_and the stable state error will be uniform convergent._  
**Proof 3**: _The Lyapunov function is defined as_  
\[V=\frac{1}{2}s^{2} \tag{26}\]  
_Then one can get that_  
\[\begin{array}{ll}\dot{V}&=s\dot{s}=\frac{s}{N+1}(\varepsilon\text{sgn}(s)+qs )\\ &\leq\frac{1}{N+1}\!\left(-\varepsilon s^{2}-qs^{2}\right)\end{array} \tag{27}\]  
_One can get that \(\dot{V}<0\) holds if inequalities (25) holds. \(\square\)_
# Escalator: An Autonomous Scheduling Scheme for Convergecast in TSCH
## 4 Proposed Autonomous Scheduling Scheme for Convergecast
### Timeslot Allocation Mechanism
* SET_TX(i,j): Node \(v_{i}\), where \(v_{i}\
eq sink\), sets \(TX_{ij}\) to \(j\times 2\) timeslot to transmit the packet generated by \(v_{j}\in SG(v_{i})\) to \(v_{k}=p(v_{i})\), the parent of \(vi\) (for example, \(TX_{23}=6\), \(TX_{24}=8\)). Figure 2 shows a schedule with a convergecast slotframe size \(L(S_{Conv})=8\) in a network consisting of four nodes with sink \(v_{1}\). In Figure 2, the gray timeslots are used to broadcast the beacon, and the red, green and blue timeslots are used to deliver packets generated by \(v_{2}\), \(v_{3}\) and \(v_{4}\), respectively. The timeslot allocation mechanism uses the above-mentioned allocation rules when a node receives RPL control messages to reflect changes in the DODAG topology into the node's timeslot schedule. Algorithm 1 shows the application of the allocation rules when a node receives RPL control messages. When a node participates in a DODAG through the reception of a DIO, the node adds TX, BT (Broadcast Transmission) and BR (Broadcast Reception) operations for transmitting the node's own packets and beacons to the convergecast slotframe. If a node already participating in the DODAG receives a DIO, the node checks whether the routing topology has changed. If the routing topology has changed, the node removes the BR operation that was used in the previous topology and adds a new BR operation for the new topology to the convergecast slotframe. When a node receives a DAO, the node adds TX and RX operations to the convergecast slotframe for forwarding packets originating from a node that sent the DAO. In Figure 2, node \(v_{4}\) sends a DAO to \(v_{2}\). When node \(v_{2}\) receives the DAO of node \(v_{4}\), node \(v_{2}\) allocates \(RX_{24}\) and \(TX_{24}\) to Timeslots 7 and 8, respectively, and delivers the DAO to node \(v_{1}\). Node \(v_{1}\) that has received the DAO of node \(v_{4}\) from node \(v_{2}\) allocates \(RX_{14}\) to Timeslot 7. Figure 2: An example of timeslot allocation for the convergecast slotframe.
## 2 Dynamic Power Management
### Dynamic Power Switching  
From 90 nm process node onwards, leakage currents have substantially gone up with as much as 30% of the total active power being contributed by the leakage power alone. This has led to the development of the power switching technique, wherein the unused logic blocks are disconnected from the power supply via a CMOS switch [5].  
Figure 2a shows the basic idea of power switching, which consists of an additional series PMOS device connecting the internal power supply node of the IP block to the on-chip power distribution grid. The PMOS power switch is sized carefully to limit the performance loss to be less than 10% [6]. During long inactive periods, the IP block is disconnected from the supply by turning OFF the PMOS switch. This reduces the leakage current by up to 40\(\times\) when compared to active mode [4]. Special attention has to be paid to the interfaces of the IP block as well as the internal state elements. Once the IP block is disconnected from the supply, the voltages on its nodes will be very poorly controlled and hence need to be isolated from the external blocks. Special, low leakage retention latches are used to store state when the IP block is disconnected from the supply [7]. The state is automatically restored upon connection back to the power supply. As an example, in a recent mobile application processor SoC only the memory and the MP3 decoder logic are kept active during MP3 audio playback, while the rest of the SoC is disconnected from the supply [4].  
This idea has also been extended to SRAM blocks with a small modification (Figure 2b). An additional NMOS switch along with a parallel diode connected NMOS transistor is inserted between the SRAM macro and the ground line [4]. During standby periods, where data retention is required, the  
Figure 1: A collection of most commonly used dynamic power management techniques.  
NMOS switch is disabled which leads to the virtual ground node slowly charging up to a value slightly above the threshold voltage of the NMOS diode transistor. This reduces the leakage in the SRAM array by a factor of 3\(\times\). During sleep mode, when data retention is not required, the power switch can be turned off, further reducing leakage power.
## 2 Methodology
### Pre-Processing
#### 2.2.2 Feature Engineering  
The process of extracting new attributes, characteristics, and properties from data is called feature engineering. The main goal of feature engineering is to design/create new features that can be used to improve the performance of the model. The features are engineered based on seismic activities indicators. The features are considered from the available literature. The detailed description about various engineered features is given as follows.  
The Gutenberg-Richter law describes the relationship between the magnitude and the number of earthquakes in a particular region [22]. The Gutenberg-Richter law states that earthquake magnitudes are distributed exponentially as:  
\[\log_{10}(N\geq m)=a-bm\ \ \ ;\ \ \ m\geq m_{c} \tag{1}\]  
Note that \(N\) represents the number of earthquakes of magnitudes of at least \(m\), such that \(m\geq m_{c}\), \(m_{c}\) is the threshold magnitude of completeness, \(b\) is referred to as the scaling parameter, and \(a\) is a constant.  
Two different methods--least square regression analysis (\(LSQ\)) and maximum likelihood (\(MLQ\))--were used to calculate the values of \(a\) and \(b\). We used both sets of techniques to identify the values of \(a\) and \(b\), and we used these values as our features for the machine learning models. The values of \(a\) and \(b\) were calculated using least square regression, and the maximum likelihood criterion was calculated using Equations (2)-(5) [23].  
\[b_{lsq}=\frac{n(\sum M_{i}\log N_{i})-\left(\sum M_{i}\sum\log N_{i}\right)}{ \left(\sum M_{i}\right)^{2}-n\sum M_{i}^{2}} \tag{2}\]  
\[a_{lsq}=\frac{\sum\left(\log_{10}N_{i}+b_{lsq}M_{i}\right)}{n} \tag{3}\]  
\[b_{mlk}=\frac{\log_{10}e}{M_{mean}-M_{min}} \tag{4}\]  
\[a_{mlk}=\log_{10}N+b_{mlk}(M_{min}) \tag{5}\]  
The mean of the earthquake magnitude was the mean value of \(n\) events, as shown in Equation (6). Prior to any large-scale earthquake, the seismic magnitude is usually rising.  
\[M_{mean}=\frac{\sum_{i}M_{i}}{n} \tag{6}\]  
Standard Deviation of b's Value  
The standard deviation of \(b\)'s value (\(\sigma_{b}\)) was established by [PERSON] and [PERSON] [24], and is calculated as shown in Equation (7):  
\[\sigma_{b}=2.30b^{2}\sqrt{\frac{\sum_{i=1}^{n}(M_{i}-M_{mean})^{2}}{n(n-1)}} \tag{7}\]Recurrence Time  
The time between two magnitudes of earthquakes equal to or greater than \(M^{\prime}\) (\(M^{\prime}\) being the value of fixed magnitude) is called the total recurrence time, and is calculated using Equation (8) [9]; it is also called the probabilistic recurrence time (\(T_{rec}\)).  
\[T_{rec}=\frac{T}{10^{a-bM^{\prime}}} \tag{8}\]  
Note that \(T\) is the length of total time under consideration.
## 4 Experiments
### Adversarial Attack Methods  
The experiments evaluate the detection accuracy of the proposed method against four well-known text adversarial attacks: PWWS, TextFooler, and DeepWordBug, which consist of three word-level and one character-level attack. Although our approach primarily focuses on detecting synonym substitution attacks, we used a simple spell checker before generating IG attributions to check whether our approach can be used together with a spell checker to detect character-level adversarial attacks.  
* PWWS--This is a word-level adversarial attack that employs a unique strategy to determine the order of word substitutions based on a word saliency weighting method, leveraging classification probabilities while maintaining the semantic integrity of the input text [23].
* TextFooler--This is a word-level adversarial attack that utilizes a word importance ranking strategy to identify key tokens in the input text. These tokens are subsequently replaced with similar words based on word embeddings, ensuring that both the semantics and syntax of the original input are preserved [22].
* BAE--This opaque (or black-box) word-level attack uses contextual perturbations from a BERT-masked language model to generate adversarial examples. In this method, it masks a portion of the text and leverages the BERT mask language model to generate possible replacements for the masked tokens, which then replace and insert in the original text [24].
* DeepWordBug--This is a character-level adversarial attack that initially identifies the most important words through a scoring strategy. It then perturbs the characters of these identified words while maintaining a minimal edit distance from the original words, effectively altering the classification output [21].