esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Biofertilizers Stimulated Date Palm Chlorophyll and Carotenoid Production under Salt Stress  
The finding reported in Table 4 showed that under salt stress, the photosynthetic pigment contents were significantly reduced (\(p<0.001\); Table 3). However, the applications of AMF, compost, and PGPR, especially their combination, increased photosynthetic pigment contents compared to the stressed control plants. Under non-stressed conditions, chlorophyll a (Chl a) content was increased by the different treatments, except for Comp, AMF\({}_{1}\) and AMF\({}_{2}\) treatments, which had no significant effects compared to the control plants (Table 4). Under the 120 mM level, AMF\({}_{2}\)+Comp, PGPR+Comp, PGPR+AMF\({}_{1}\), PGPR+AMF\({}_{2}\), PGPR+AMF\({}_{1}\)+Comp and PGPR+AMF\({}_{2}\)+Comp significantly increased the Chl a content compared to control plants. However, under the 240 mM level, no significant difference was recorded between the applied treatments and the control plants. As for Chlorophyll b (Chl b) content, it was increased by AMF\({}_{2}\), PGPR, PGPR+Comp, PGPR+AMF\({}_{1}\), PGPR+AMF\({}_{1}\)+Comp and PGPR+AMF\({}_{2}\)+Comp treatments under non-stressed conditions (Table 4). The two tripartite combinations PGPR+AMF\({}_{1}\)+Comp and PGPR+AMF\({}_{2}\)+Comp were the most effective at improving Chl b by 158% and 157%, respectively, compared to the control. For the 120 mM level, PGPR+AMF\({}_{1}\), PGPR+AMF\({}_{2}\), PGPR+AMF\({}_{1}\)+Comp and PGPR+AMF\({}_{2}\)+Comp treatments significantly increased the Chl b amount by 73%, 83%, 131% and 141% respectively, compared to control plants. Under 240 mM level, PGPR, AMF\({}_{2}\)+Comp, PGPR+Comp, PGPR+AMF\({}_{1}\), PGPR+AMF\({}_{2}\), PGPR+AMF\({}_{1}\)+Comp and PGPR+AMF\({}_{2}\)+Comp treatments increased the concentration of Chl b compared to the control. According to the total chlorophyll content, the AMF\({}_{2}\)+Comp, PGPR+Comp, PGPR+AMF\({}_{1}\), PGPR+AMF\({}_{2}\), PGPR+AMF\({}_{1}\)+Comp and PGPR+AMF\({}_{2}\)+Comp treatments significantly increased this parameter independently of salinity level (Table 4). However, PGPR and AMF\({}_{1}\)+Comp treatment increased total chlorophyll content under non-stressed conditions and 240 mM salinity level. The PGPR+AMF\({}_{1}\)+Comp and PGPR+AMF\({}_{2}\)+Comp treatments recorded the highest increments (75% and 77% respectively) under non-stressed conditions compared to the control plants. Concerning the carotenoid content, under non-stressed conditions, all treatments enhanced this parameter except for Comp, AMF\({}_{1}\) and AMF\({}_{2}\) treatments, which showed no significant effect on carotenoid content compared to the control plants (Table 4). However, under stressed conditions, bipartite and tripartite combinations (PGPR+AMF\({}_{1}\), PGPR+AMF\({}_{2}\), PGPR+AMF\({}_{1}\)+Comp and PGPR+AMF\({}_{2}\)+Comp) significantly increased the carotenoid content compared to control plants under 120 mM salinity level.
# Developing a Generalized Regression Forecasting Network for the Prediction of Human Body Dimensions
## 4 Experimental Results and Discussion
### Experimental Setup  
#### 4.1.1 Description of Experimental Data  
Following Ref. [6], [PERSON] et al. utilized the Vitus Smart 3D body scanning device to create a dataset comprising measurements from 106 female undergraduate students, never pregnant, aged 20 to 25 years, in the northeastern region of China. The participants' heights ranged from 151.5 to 173.2 cm, and their weights ranged from 40 to 71 kg based on intermediate sizes provided by the Chinese National Standard, GB/T 1335.2-2008: [42] Standard Sizing Systems for Garments-Women. Prior to commencement of this study, we addressed ethical compliance issues and made relevant disclosures. Notably, all participants provided informed consent, allowing their measurement data to be obtained using a 3D body scanner in an automated manner. The final measurement dataset exclusively provided our measurement testing data, and we completely anonymized the data items. Hence, no personally identifiable participant information was retained. Furthermore, our reference few-shot anthropometric database is publicly available, as explained in appendix 1 in reference [43]. To this end, we utilized this open-source anthropometric database to validate the efficacy of our hybrid model. Table 1 displays the descriptive statistics of the body dimensions and relevant measures, such as mean, median, and central tendency.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Measurements** & **Abbr.** & **Min** & **Max** & **Mean** & **Median** & **SM** & **SD** & **CV (\%)** \\ \hline \hline \multirow{2}{*}{
\begin{tabular}{c} Stature \\ Waist Height \\ Abdomen Height \\ HH \\ \end{tabular} } & ST & 151.5 & 173.2 & 161.9 & 160.9 & 161.4 & 5.21 & 3.22 \\  & WH & 93.4 & 111.0 & 101.2 & 100.5 & 100.8 & 3.97 & 3.92 \\  & AH & 86.2 & 103.3 & 94.5 & 94.1 & 94.2 & 3.80 & 4.02 \\  & HH & 75.2 & 91.1 & 82.9 & 82.4 & 82.7 & 3.76 & 4.54 \\  & CH & 64.8 & 80.7 & 71.7 & 71.2 & 71.5 & 3.29 & 4.59 \\  & Knee Height & KH & 39.0 & 48.1 & 43.5 & 43.3 & 43.3 & 2.04 & 4.70 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive statistics on dimension measurements.
# Synergistic Potential of Optical and Radar Remote Sensing for Snow Cover Monitoring
## 2 Study Sites  
The Iberian Peninsula features highly irregular orography characterized by extensive mountainous massifs spread across its main snow-dominated mountain ranges (Cantabrian System, Central System, Iberian Range, Pyrenees, and Sierra Nevada) (Figure 1). Positioned between the Atlantic Ocean and the Mediterranean Sea, together with its intricate topography, the Iberian Peninsula experiences a diverse climatic regime. Precipitation exhibits a notable negative gradient from the northwest to the southeast, with the northwestern quadrant receiving an annual average precipitation exceeding 1000 mm [47], mainly influenced by the North Atlantic Oscillation and the Arctic Oscillation [48]. In contrast, the southeastern quadrant is dominated by the Western Mediterranean Oscillation with annual average precipitation records below 300 mm. On the other hand, temperature displays a discernible positive gradient from north to south and from coastal areas towards inland regions [49]. Winter temperatures are frequently colder than 0 \({}^{\circ}\)C in the main mountain ranges, depending on elevation. The substantial climatic variations, along with differences in latitude, elevation, and proximity to the coast among the main mountain ranges of the Iberian Peninsula, significantly influence SCA distribution, snow season length, and the dynamics of the melting-freezing processes during the snow season.  
Figure 1: Location of the main snow-dominated mountain ranges of the Iberian Peninsula. The circle points indicate the centroid of each mountain range.
## 4 Results
### EASY Concept Design Details: System Functionality for Different Users
#### 4.4.2.3 Map Viewing, Query and Printing of Base Datasets  
It is envisioned that EASY will be an online location where landholders can easily view basic aerial and topographic information about their properties through unfettered access to the base functions and datasets. EASY would provide basic tools for landholders to view spatial data, measure areas and distance, record their own features of interest and print maps of their property or region.
## 3 Results
### Number of Classes and Generalization  
Table 6 shows 1D-CNN performance at different number of classes. In Table 6, it can be seen that the lowest accuracy (50%) is reached using four classes. In comparison, 80% accuracy is achieved using six classes. Four and five categories were selected for the training process because, in real applications, a huge number of unknown signals per second are obtained, and network performance needs to be at least acceptable under these training conditions.  
The proposed 1D-CNN shows great generalization capability, since even when training with only four failure categories (Figure 9), the network can classify unknown faults into identifiable categories. For example, if the network is trained through the moderate IR failures, it correctly classifies severe IR failures. This indicates that the network has the ability to optimally learn different feature categories, as long as enough training data are provided. In essence, ID-CNN is achieving classification and clustering simultaneously.
# A Two-Stage Household Electricity Demand Estimation Approach Based on Edge Deep Sparse Coding
## 4 Results  
### Description of The Dataset  
The data used to verify the validity of the proposed method were obtained from AMPds dataset and REFIT dataset. AMPds dataset comprehensively records the minutely current readings of a Canadian household and its 19 appliances for 730 days. The data was collected from a house in the Greater Vancouver metropolitan area in British Columbia (Canada) and has been cleaned to provide for consistent and comparable accuracy results amongst different researchers and machine learning algorithms. The house has one level above grade and a basement making up a total of 199 m\({}^{2}\). The main floor ceiling height is 2.44 m and the basement ceiling height is 2.13 m. The house has a family of three persons: A male and a female adult in their late 30 s and a daughter between the age of 5 and 6. The male adult is a full-time student at a local university, the female adult is self-employed, and the child attends full-time elementary school [33]. The minutely electricity consumption in the original dataset has been converted to consumption for every 30 min to match the common measurement frequency of existing smart meters and to obtain realistic results. Therefore, the original load matrix is \(48\times 730\).  
This study selected the load profiles of three representative household appliances (heat pump, cloth washer and television) obtained from submetering for four random weeks to create the initial dictionary, which was a \(48\times 84\) matrix. The test experiment was implemented on Matlab and Python platforms.
## 5 Conclusions  
This study analyzed short-term trends of Sentinel-2, Planet Scope, and Landsat, as well as the NDVI, NDWI, GNDVI, GLI, EVI, DVI, SAVI, MSAVI, and GSAVI, and studied the correlations and regression models between these different sources of vegetation indices in small mountain grasslands. For this purpose, from the May to July 2018-2023 monthly averages, particular days of VIs were used.
## 6 Other Taxonomies
### Anchor-Based vs. Anchor-Free  
In an anchor-based scheme, we have information about the location of some nodes or BS called anchors obtained either via GPS or by manually deploying them. This information and measurement are used as input for the localisation algorithm. In the anchor-freemethod, however, there is no information about the positions of the nodes. While anchor-based are more accurate, anchor-free approaches are advantageous because they are more scalable and can remove the process of anchor deployment [78; 79].
## 3 Results and Discussion
### Effect of Adsorbent Dosage  
The amount of Sb(V) adsorbed amounts per unit mass of adsorbents and the removal percentage under different amounts of Fe-CMB are presented in Figure 6. With an increase in the Fe-CMB dose from 3.3 g/L to 16.7 g/L, the amount of Sb(V) adsorbed per unit mass of Fe-CMB decreased from 13.8 mg/g to 3.0 g/L, because the adsorption sites present on the adsorbent surface were not fully occupied by adsorbate [19]. However, at higher Fe-CMB doses, the removal percentage of Sb(V) increased because the increase of adsorbent dosage provided more available sites for Sb(V) adsorption, thereby leaving less residual concentration of Sb(V) in the aqueous phase [35]. Approximately 98.5% of Sb(V) could be removed by Fe-CMB at a dose of 10.0 g/L.
# Research on Routing Algorithm of Construction Robot Cluster Enhanced Ad Hoc Network
## 2 Problem Statement and System Model
### Node Movement Model  
The wireless self-organizing network models were classified into individual and group mobility models according to the mobility mode [32]. Since the construction robots operate on different floor planes, the node individual movement model is designed to move only on the plane. Each node has an initial velocity and an initial direction, which are recalculated each time a new job is performed, and the node is updated with its velocity according to Equation (1):  
\[v_{n}=a\times v_{0}+(1-a)\times v_{a}\times\sqrt{1-a^{2}}\times v \tag{1}\]where \(v_{0}\) denotes the initial velocity, \(a\) is the parameter regulating the randomness, \(v_{\rm a}\) denotes the average velocity, and \(v\) denotes the velocity parameter obeying a Gaussian distribution. The nodes are updated in the velocity direction according to Equation (2):  
\[d_{\rm n}=a\times d_{0}+(1-a)\times d_{\rm a}+\sqrt{1-a^{2}}\times d \tag{2}\]  
where is the \(d_{0}\) initial velocity direction, \(d_{\rm a}\) is the average velocity direction, and \(d\) is the velocity direction parameter that obeys Gaussian distribution. The nodal coordinates of the node can be derived from the above Equation (3):  
\[\left\{\begin{array}{l}X_{\rm in}=X_{i0}+v_{\rm n}\times\cos(d_{\rm n}) \times t\\ Y_{\rm in}=Y_{i0}+v_{\rm n}\times\sin(d_{\rm n})\times t\\ Z_{\rm in}=Z_{\rm in}\end{array}\right. \tag{3}\]  
where \(X_{i0}\), \(Y_{i0}\), and \(Y_{i0}\) denote the initial position coordinates of the node, and \(X_{\rm in}\), \(Y_{\rm in}\), and \(Z_{\rm in}\) denote the position coordinates of the node after moving \(t\) moments.
## 3 The End-to-End Instrument Performance Simulation System (EIPS) Framework
### EIPS Components
#### 3.2.3 Sensor Component of the HT-FRTC  
The advantage of using a fast mode of the HT-FRTC within the EIPS framework is that it does not require instrument-specific coefficients, in comparison to conventional fast radiative transfer models like the Radiative Transfer for TOVS (RTTOV; TOVS stands for TIROS Operational Vertical Sounder and TIROS for Television InfraRed Observation Satellite) [27]. The fast mode uses a small number of about 300 monochromatic frequencies as predictors for the PC scores. The PCs themselves are derived during the training at a very high spectral resolution (HT-FRTC calculations were done at a spectral resolution of 3 MHz) and are independent of any instrument parameters. Simulated spectra for any particular instrument can be constructed by the convolution with the sensor SRF either on the level of radiances or on the level of PCs. Convolution of highly spectrally radiances with instrument specific SRFs offers the flexibility of taking any type of instrument line shape only at the last step of the process. This process of construction of instrument simulated spectra using convolution of highly spectrally resolved radiances with sensor SRFs is represented in the flow diagram in Figure 5. This specific property of HT-FRTC makes it very useful for studies related to new instruments and for investigating large number of instrument configurations.  
Figure 4: Comparison of fast and line mode of the EIPS framework radiative transfer model, the HT-FRTC: (**a**) Brightness temperature bias (calculated for fast code compared with the line HT-FRTC code). (**b**) Brightness temperature standard deviation between fast and line HT-FRTC code.
# Model Checking Resiliency and Sustainability of In-Vehicle Network for Real-Time Authenticity
## 6 Implementation and Experiments
### Reception Time of an AFR Message
#### 6.2.2 Reception Time of an AFR Message under BoA  
BoA on the authenticator causes the transmission delay of an AFR message. In general, the continuous BoA can permanently interfere with the communication from an ECU. However, since the authenticator of MAuth-CAN has two CAN controllers, it is possible for the authenticator to put a malicious ECU that performs the BoA into Passive Error mode which allows the transmission of an AFR message from the authenticator.  
The time it takes for the malicious node performing BoA to become the error passive state varies depending on the attack bit position for the BoA (i.e., a bit-error position in the data field of an AFR message). If the malicious node performing BoA creates the first bit-error at the first bit position in the data field of an AFR message, the reception time of an AFR message is approximately 2355 ms as shown in Figure 15b. In other hands, to maximize the transmission delay of an AFR message by the BoA, the malicious node performing BoA can create a bit-error at the last bit position (i.e., 64 th bit position in the data field) of an AFR message. In this the worst case, the reception time of an AFR message is approximately 4495 ms as shown in Figure 15c.  
Through this experiment, we show Theorem 2 by validating that Passive Error mode of the malicious node performing BoA on the authenticator allows the authenticator with dual CAN-controllers to transmit an AFR message within the bounded time and the worst case time is 4495 ms.
# Cold Boot Attacks on the Supersingular Isogeny Key Encapsulation (SIKE) Mechanism
## 3 Supersingular Isogeny Key Encapsulation Mechanism
### Mathematical Foundations
#### 3.1.4 Isogenies  
Let \(E_{1}\) and \(E_{2}\) be elliptic curves over a finite field \(\mathbb{F}_{q}\). An isogeny \(\Phi:E_{1}\to E_{2}\) is a non-constant rational map defined over \(\mathbb{F}_{q}\), with \(\Phi\) being a group homomorphism from \(E_{1}\big{(}\mathbb{F}_{q}\big{)}\) to \(E_{2}\big{(}\mathbb{F}_{q}\big{)}\). Additionally, \(\Phi\) can be written as \(\Phi(x,y)=(f(x),y\cdot g(x))\), where \(f\) and \(g\) are rational maps over \(\mathbb{F}_{q}\). Note that we can write \(f(x)=p(x)/q(x)\) with polynomials \(p(x)\) and \(q(x)\) over \(\mathbb{F}_{q}\), where \(p(x)\) and \(q(x)\) do not have a common factor, and similarly for \(g(x)\). We define the degree \(deg(\Phi)\) of the isogeny to be \(max\{deg(p(x)),deg(q(x))\}\), where \(p(x)\) and \(q(x)\) are as above. Given an isogeny \(\Phi:E_{1}\to E_{2}\), \(ker(\Phi)=\{P\in E_{1}\,:\,\Phi(P)=O\}\) denotes the kernel of \(\Phi\). Additionally, given a finite subgroup \(H\leq E_{1}\big{(}\mathbb{F}_{q}\big{)}\), there is a unique isogeny (up to isomorphism) \(\Phi:E_{1}\to E_{2}\) such that \(ker(\Phi)=H\) and \(deg(\Phi)=|H|\), where the curve \(E_{2}\cong E_{1}/H\). We can compute the isogeny \(\Phi\) and the curve \(E_{1}/H\) for a given a subgroup \(H\leq E_{1}\big{(}\mathbb{F}_{q}\big{)}\), using [PERSON]'s formula [39]. However, it is computationally impractical for arbitrary subgroups. SIKE uses isogenies over subgroups that are powers of \(2\), \(3\) and \(4\)[1].
## 5 Algorithm Design
### Complexity Analysis  
The training of models is usually performed offline, while the computational complexity of each agent during execution depends on the combination of neural network forward computations. The proposed multi-modal-MAPPO framework involves multiple neural  
Figure 2: Architecture diagram of multi-modal-MAPPO.  
networks. For the image processing branch, the computational complexity of each CNN unit is denoted as \(\mathcal{O}\{\mathcal{M}^{2}\cdot\mathcal{N}^{2}\cdot\mathcal{C}_{in}\cdot \mathcal{C}_{out}\}\), where \(\mathcal{M}\) and \(\mathcal{N}\) represent the dimensions of the output feature map and convolutional kernel size, and \(\mathcal{C}_{in}\) and \(\mathcal{C}_{out}\) denote the number of input and output channels, respectively. For the attribute processing branch, the three-layer MLP used for encoding attribute features has a computational complexity of \(\mathcal{O}\{\mathcal{I}_{in}\cdot\mathcal{I}_{out}\}\) for each fully connected layer, where \(\mathcal{I}_{in}\) and \(\mathcal{I}_{out}\) are the input and output sizes of the layer. The LSTM model used to capture temporal patterns in user cache states has a computational complexity of \(\mathcal{O}\{\mathcal{N}_{in}\cdot\mathcal{N}_{h}+\mathcal{N}_{h}^{2}\}\), where \(\mathcal{N}_{in}\) is the input feature dimension and \(\mathcal{N}_{h}\) is the hidden state dimension. The attention mechanism involves matrix operations to compute attention weights. For input vectors of dimension, the complexity of the fully connected layers in the attention mechanism is \(\mathcal{O}\{d^{2}\}\), and the softmax operation adds \(\mathcal{O}\{d\}\) complexity. Overall, the attention mechanism has a complexity of \(\mathcal{O}\{d^{2}+d\}\). The overall complexity of the MAPPO algorithm depends on the number of agents \(K\) and the complexity of the actor and critic networks. The critic network is a three-layer MLP, with each fully connected layer having a complexity of \(\mathcal{O}\{\mathcal{H}_{in}\cdot\mathcal{H}_{out}\}\), where \(\mathcal{H}_{in}\) and \(\mathcal{H}_{out}\) are the input and output sizes of the layer. Therefore, the overall computational complexity of the proposed multi-modal-MAPPO framework can be summarized as  
\[\mathcal{O}\{K\cdot(\sum_{v=1}^{V}\mathcal{M}^{2}\cdot\mathcal{N}^{2}\cdot \mathcal{C}_{v-1}\cdot\mathcal{C}_{v}+\mathcal{N}_{in}\cdot\mathcal{N}_{h}+ \mathcal{N}_{h}^{2}+\sum_{y=1}^{V}\mathcal{I}_{y-1}\cdot\mathcal{I}_{y}+\sum_ {z=1}^{Z}\mathcal{H}_{z-1}\cdot\mathcal{H}_{z})\} \tag{32}\]  
where \(V\) is the number of layers in the CNN networks, and \(Y\) and \(Z\) are the number of fully connected networks in the actor network and critic network, respectively.
## 4 Simulations Results and Discussion with Performance Evaluation
### Simulation Results
#### 4.3.1 Active Nodes per Layer  
Several active nodes are an important parameter that effect the network lifetime. The most important key parameter used to prolong the network lifetime is the number of active nodes. Figure 9 shows the number of active nodes per layer for both proposed and existing protocols. In existing protocols, the number of active nodes increases with the increase in number of layers. Because if the source node lies near the surface gateway, it results in fewer active nodes and awakes fewer nodes. In both FLMPC-One and FLMPC-Two, multiple routes are originated from every cross node to form a binary tree for reliable data delivery. This binary tree generation actives a large number of nodes. In the FLMPC-One, active nodes are more than the FLMPC-Two. The number of active nodes increases, while moving from lower to the upper layer of the underwater network to forward the data packets. The main reason for the increase in the number of active nodes are the cross nodes. The cross nodes originate multiple copies of data packets and generate a binary tree for reliable data delivery. In FLMPC-Two, the number of active nodes are less than the FLMPC-One because in FLMPC-One, a source node looks forward up to 2-hop which leads the data packet in void region. Meanwhile, FLMPC-Two looks forward up to 3-hop and reduces the probability of packet delivery in void hole region. The aforementioned idea minimizes retransmissions and the number of active nodes.  
In contrast, the SPF-Three finds the shortest path to reach the destination by avoiding void hole regions completely. Therefore, active nodes in SPF-Three are less than the existing protocols. Meanwhile, the BFS-SPF-Three finds the different possible shortest paths in breadth wise and performs opportunistic routing by selecting the best potential-forwarder nodes. The aforementioned route selection mechanism efficiently minimized the EC. It contains the fewer active nodes and minimum distance. The number of active nodes in each scheme is shown in Figure 9. Figure 9 clearly depicts that active nodes in BFS-SPF-Three are lesser than SPF-Three and the aforementioned existing protocols. Both proposed protocols outperformed. It makes smart decisions for the reliable route selection. Therefore, suitable nodes are selected for communication to prolong the network lifetime.