esa-sceva/satcom-chunk-collection huggingface dataset:
# Land Cover Mapping in Southwestern China Using the HC-MMK Approach
## 3 Methodology
### Five-Step Interactive Quality Control Based on Knowledge
#### 3.7.3 Step Three: Spatial Consistency Verification  
The spatial discontinuity phenomena of land cover often occurred at the junction regions of the neighbor mapping units in classification results, which were mainly caused by the following differences: the acquisition time of selected satellite images in neighboring mapping units and the cognitive level of mappers. Therefore, the spatial consistency verification is indispensable to assuring the quality of the CLC-SW2010 product. To avoid any inspection omission, the whole of Southwestern China was divided into 24,130 grids with a side length of 10 km. The experienced mapper carefully checked and revised the spatial discontinuous phenomena grid by grid.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Types** & **Detailed Description** \\ \hline \multirow{4}{*}{Single-temporal rules} & Open waters and wetlands reside in a relatively flat or low relief areas \\  & The spatial distribution of each forest class commonly below the tree line \\  & The spatial distribution of glaciers and permanent snow usually under the snow line \\  & Croplands rarely distribute in the high-elevation region, such as above 4000 m \\  & Paddy fields locate close to the water source and relatively flat areas \\ \hline \multirow{4}{*}{Multi-temporal rules} & The deciduous vegetation has obvious different spectral characteristics between leaf-on and \\  & leaf-off season, but the evergreen vegetation does not have this differences \\  & Open waters have great fluctuations in rainy season, the boundary of open water is the \\  & maximum boundary in rainy season \\  & Snow and ices have great fluctuation in winter, the boundary of ice and permanent snow is \\  & the minimum boundary in summer \\ \hline \hline \end{tabular}
\end{table}
Table 3: The geographical rules used in the interactive quality control.
## 3 Results
### Three-Dimensional Structure Prediction of the GPD2 GPP Domain  
In order to explore potential conformational changes triggered by salinity stress, the GPP domain structure was predicted by homology modeling [41] using the crystal structures of _D. salina_ osmoregulated bidomain GPDH (Figure 6A) or human phosphoserine phosphatase (PSP) (Figure 6B,E) as templates. As described for these two enzymes [17; 42], the GPD2 GPP domain appears to consist of a Rossman-fold-like motif with alternating \(\alpha\)-helices and \(\beta\)-strands. This motif was modeled with a high degree of confidence, and corresponds to the putative catalytic core of the enzyme, including a Mg\({}^{2+}\) atom (Figure 6A,B,E). Modeled with lower confidence was an \(\alpha\)-helical cap domain (Figure 6A,B) that, in the haloacid dehalogenase (HAD) superfamily of enzymes, is used to shield the active site [42]. Detailed structural studies of the PSP family indicated that substantial conformational rearrangements of the active site (from open to closed conformation of the cap domain upon substrate binding) are required to catalyze the reaction [42; 65]. An \(\alpha\)-helix from the cap domain closes (and completes) the active site after phosphoserine binding, modeled for GPD2 GPP by using as template hPSP 6 hyj.1.A (Figure 6B,D, \"closed\" cap domain). After catalysis, the \(\alpha\)-helix unfolds into an open loop, allowing the products to leave the enzyme, modeled for GPD2 GPP by using as template hPSP 6 hyj.2.A (Figure 6E, \"open\" cap domain). The \"best fit\" GPD2 GPP structure, modeled by using as template _D. salina_ osmoregulated bidomain GPDH 6 hyj.1.A (Figure 6A,C), predicts a cap domain conformation somewhere in between the \"closed\" and \"open\" models. However, in the crystal structure of the _Dunaliella_ enzyme (6 iivy.1.A) electron density was missing for residues 248-256, and the folding of the cap domain was not completely solved [17]. Artificial intelligence-based structure prediction with the AlphaFold algorithm (43) resulted in a model (Figure S3D) with somewhat greater similarity to the \"closed\" cap domain. Nonetheless, these structural modeling analyses suggest that extensive conformational rearrangements may characterize an active GPD2 GPP domain.
# Distributed GNE-Seeking under Partial Information Based on Preconditioned Proximal-Point Algorithms
## 4 Distributed Algorithm with Partial Information
### Convergence Analysis
For all \(k\in\mathbb{N}\),  
\[\left\|h^{k}-v^{*}\right\|_{\Phi}^{2}=\left\|v^{k}-v^{*}\right\|_{\Phi}^{2}-2 \beta^{k}\!\left\langle v^{k}-u^{k}\mid v^{k}-v^{*}\right\rangle_{\Phi}+\left( \beta^{k}\right)^{2}\!\left\|u^{k}-v^{k}\right\|_{\Phi}^{2}. \tag{41}\]  
Then by Lemma 7, we have  
\[\left\|h^{k}-v^{*}\right\|_{\Phi}^{2}\leq\left\|v^{k}-v^{*}\right\|_{\Phi}^{2}- \gamma^{k}\!\left(2-\beta^{k}\right)\!\left\|u^{k}-v^{k}\right\|_{\Phi}^{2}. \tag{42}\]By the Cauchy-Schwarz inequality, it holds that  
\[\left\|h^{k}-v^{*}\right\|_{\Phi}\leq\left\|v^{k}-v^{*}\right\|_{\Phi}. \tag{43}\]  
By (43), the sequence \(\left(\mathbf{v}^{k}\right)_{k\in\mathbb{N}}\) is bounded so that there is at least one cluster point such as \(\bar{v}\). Define:  
\[\overline{\mathbb{A}_{1}}\Bigg{[} \begin{array}{c}\mathbf{x}\\ \mathbf{z}\\ \mathbf{\lambda}\end{array} \Bigg{]}=\left[ \begin{array}{c}\mathbf{F}_{\mathrm{c }}(\hat{\mathbf{x}})\\ 0\\ \mathbf{\mathrm{b}}\end{array} \right]+\left[ \begin{array}{c}\mathbf{R}^{\mathrm{T}} \mathbf{A}^{\mathrm{T}}\mathbf{\lambda}\\ L\mathbf{\lambda}\\ -AR\hat{x}-\mathbf{\mathrm{L}}z\end{array} \right]. \tag{44}\]  
\(\overline{\mathbb{A}_{1}}\) consists of the sum of the first two equations of \(\overline{\mathbb{A}}\). By (29) and (44), it holds that  
\[\left\langle\overline{\mathbb{A}_{1}}\Big{(}u^{k}\Big{)}+\Phi\Big{(}u^{k}-v^{ k}\Big{)}\Big{|}v-u^{k}\right\rangle\geq 0. \tag{45}\]  
Let \(\eta:=\sup_{k\in\mathbb{N}}\left\|v^{k}-v^{*}\right\|_{\Phi}<\infty\) and \(\epsilon^{k}:=2\eta\left(\beta^{k}\Big{\|}\epsilon^{k}\Big{\|}_{\Phi}\right)+ \left(\beta^{k}\Big{\|}\epsilon^{k}\Big{\|}_{\Phi}\right)^{2}\), for all \(k\in\mathbb{N}\). Then, \(\left(\epsilon^{k}\right)_{k\in\mathbb{N}}\in\ell^{1}\). Moreover, for all \(k\in\mathbb{N}\) we have  
\[\left\|v^{k+1}-v^{*}\right\|_{\Phi}^{2} \tag{46}\] \[\leq \Big{(}\Big{\|}h^{k}-v^{*}\Big{\|}_{\Phi}+\beta^{k}\Big{\|} \epsilon^{k}\Big{\|}_{\Phi}\Big{)}^{2}\] \[\leq \Big{\|}v^{k}-v^{*}\Big{\|}_{\Phi}^{2}-\beta^{k}\Big{(}2-\beta^{ k}\Big{)}\Big{\|}u^{k}-v^{k}\Big{\|}_{\Phi}^{2}+\epsilon^{k}.\]  
Followed by recursion, it holds that  
\[\left(\beta^{k}\Big{(}2-\beta^{k}\Big{)}\Big{\|}u^{k}-v^{k}\Big{\|}_{\Phi}^{2 }\right)_{k\in\mathbb{N}}\in\ell^{1}. \tag{47}\]  
By (47), \(u^{k}-v^{k}\) converges to \(\mathbf{0}\) as \(k\to\infty\). Let \(l_{k}\) be a divergent subsequence such that \(v^{l_{k}}\to\bar{v}\). Then, for any \(v\in\Omega\times\mathbb{R}^{m}\times\mathbb{R}^{m}_{\geq 0}\), we have \(\left\langle\overline{\mathbb{A}_{1}}(\bar{v})\mid v-\bar{v}\right\rangle\geq 0\) by continuity of \(\overline{\mathbb{A}_{1}}\), which means \(\bar{v}\in\mathrm{zer}(\overline{\mathbb{A}1})=\mathrm{fix}\Big{(}\mathrm{J}_{ \Phi^{-1}}\overline{\mathbb{A}_{1}}\Big{)}\). By Lemma 6, any cluster point of \(v^{k}\) belongs to \(C\). Hence, \(v^{k}\) converges to an equilibrium of (29).
## 3 Numerical Analysis
### Designs Returned by the Algorithm WBGEA_1t_nc  
Figure 9 gathers the best designs found by the WBGEA_1t_nc (Wasserstein-based, ternary mutation of the size, no crossover, as explained in Table 2) variant of the algorithm. These designs are also the best overall observed solutions for the test functions. The best observed solutions correspond to what is produced by a stochastic, evolutionary algorithm after 500 iterations with a population size of \(\mu=300\) and an initial population of 300, which sums up to 150,300 calls to the objective function. They are not fully converged and could be fine tuned by a local search. We have chosen to show these solutions, produced by the evolutionary algorithms without local fine tuning, for a more realistic reporting of their performance. It can be seen that the designs are consistent with the simulated physical phenomena. The points are placed optimally according to the wind directions for the wind-farm functions. For instance, concerning \(F_{0}\)'s best design, we see that the vertical distances between the points are smaller than the horizontal ones, and there is an alignment of some of the wind turbines at the domain boundary where the wind enters (\(x=-40\)). The optimal number of points (i.e., turbines) in the set is \(n_{\max}=20\), as all possible turbines are used to maximize power production. The best observed design for \(F_{90}\) is approximately a \(90^{\circ}\) rotation of the \(F_{0}\) solution. The solutions to \(F_{45}\) and \(F_{4d}\) are not rotations of the solutions \(F_{0}\) or \(F_{90}\) because the square domain \(\mathbf{D}\) is not rotation invariant for these angles. The best designs for \(F_{intert}\) have \(n=n_{\max}\) points located in the corners, where they contribute the most to the total inertia. The optimal design for \(F_{minDist}\) has, logically, \(n_{\min}=10\) points that are spread over all of the domain.  
Figure 9: Best observed designs corresponding, respectively, to the test cases \(F_{0}\), \(F_{90}\), \(F_{45}\), \(F_{4d}\), \(F_{intert}\) and \(F_{minDist}\) (left to right, top to bottom).
# Fine-Grained Boundary Conditions in Field-Based Routing
## 1 Introduction
These outer boundary conditions enable applicability to various network topologies and remain resilient even in node failure scenarios. On the other hand, the proposed inner boundary conditions assign different potential values to gateways based on the traffic load levels around them to achieve better load balancing. In summary, our research aims to enhance the performance of field-based routing by adopting the proposed fine-grained boundary conditions, which are designed to address the practical limitations of traditional field-based routing protocols. The organization of the paper is as follows. In Section 3, we briefly review field-based routing. Then, we discuss the limitations of field-based routing in Section 4. Section 5 describes newly proposed advanced boundary conditions. In Section 6, we evaluate our proposed scheme in comparison with the existing baseline model. Finally, we conclude the paper in Section 7.
## 7 Discussion of Quality Properties
### First Scenario  
The first scenario offers a high degree of **Availability**, since there is no single point of failure in the greenhouse. In particular, if one sensor is unavailable, the others will continue to sense and produce temperature data and thus the temperature value. The condition in which the service is unavailable is when all the sensors installed in the greenhouse stop working.  
On top of that, the scenario offers a fault-tolerant service since, in any case in which the condition mentioned earlier is not met, the service will continue its intended operation. Furthermore, it is noteworthy that the user knows the sensors that are not in operation, since they can see each sensor when it sends data for the last time. So the user can fix physical-world hardware faults when they arise.  
The **Time Behavior** is served at a high degree. More specifically, the response time is low, and the turnaround time is also low since, in IoT, minimal and fundamental stateless transformations will occur. Therefore, the total time for generating the temperature value is short. In addition, given that the processing in the Event Engine is simple and fast and that the Event Bus of the architecture offers extremely high throughput, the scenario has high throughput to boot.  
However, there is a non-ideal condition that, when satisfied, affects the timeliness of the generated temperature value. Specifically, the user makes decisions based on the ingestion timestamp instead of the event timestamp. As a result, the timestamp of the generated \"meaningful\" event will be the ingestion timestamp of the IoT Event. So when a sensor is late in sending data, the Event Engine produces a \"meaningful\" event with a correct and valid temperature value based on the concepts defined by reliability. Nevertheless, this value differs from the actual one because time has passed. It is recalled that, in any case, the user is aware of this information and can either take additional actions or wait for the next actual temperature value, which, based on this scenario, will be very soon.  
Moreover, this scenario offers a relatively high degree of **Time Behavior**: It is inclusive, since it takes into account all the sensors, even though they do not collectively contribute to the calculation of the temperature value. The validity and correctness of the input data always result in the production of a valid and correct temperature value. Given that the condition mentioned previously in the availability analysis is maintained, i.e., at least one sensor is available, the incoming events from the physical environment are always sufficient to calculate the temperature value.  
At the same time, the functional correctness is served to the absolute degree, since when at least one sensor is available (i.e., there is not a total lack of sensors), business continuity of the service is guaranteed. Nevertheless, it is evident that when one of the two non-ideal conditions arises, the \"meaningful\" events resulting from the service may not be trustworthy for the user.
## 4 Forecasting Methods
### Bidirectional Long Short-Term Memory  
The bidirectional RNN (BRNN), which was firstly introduced by [PERSON] and [PERSON] in [31], utilizes both past and future information to determine the output values of an ordinary time series. Different from the LSTM network, in which merely previous information has an effect on output, this approach can be trained without the constraints of using a predefined input length. In the BRNN architecture, two different hidden layer nodes exist and these layers are connected with both input and output layers. While the recurrent unit of the first hidden layer has a connection with previous time steps through the forwarding direction, a similar connection is carried out in the opposite direction at the second hidden layer.  
The BRNN can be trained with the same method used in a standard RNN. A standard feed-forward network unfolded through time can also be preferred due to the nonoccurrence of any interaction between hidden layers. The mathematical equations of BRNN are given by (11) to (13)  
\[h_{t} = \sigma(w_{h,x}x_{t}+w_{h,h}h_{t-1}+b_{h}) \tag{11}\] \[e_{t} = \sigma(w_{e,x}x_{t}+w_{e,e}e_{t+1}+b_{e})\] (12) \[y_{t} = f(x)_{i}\times\Bigl{(}w_{y,h}h_{t}+w_{y,e}e_{t}+b_{y}\Bigr{)} \tag{13}\]  
where \(h_{t}\) and \(e_{t}\) represent the values of hidden layers in both the forward and backward directions of BRNN, respectively. Therefore, the LSTM and BRNN methods are suitable methods that can be used together. The LSTM method can compose a new unit that can be used in the hidden layer, while the BRNN method establishes a connection between these units. This approach is termed BLSTM and has been utilized in [32] for handwriting recognition and frame-wise phoneme classification.
# Joint Cell Association and User Scheduling in Carrier Aggregated Heterogeneous Networks
## 4 Numerical Analysis
### Simulation Configuration  
In a typical two layer Heterogeneous Network, the carrier bands for aggregation is set as \(N=2\), with one high frequency carrier band \(C^{1}\) and one low frequency carrier band \(C^{2}\). The carrier bands deployments are denoted by a binary matrix between different network layers and different carrier frequencies as in \([\alpha_{11}\ \alpha_{12};\alpha_{21}\ \alpha_{22}]\), where  
\[\alpha_{i_{1}i_{2}}=\left\{\begin{array}{ll}1,&\text{carrier $i_{2}$ is deployed on HetNet layer $i_{1}$},\\ 0,&\text{otherwise}.\end{array}\right. \tag{18}\]  
For example, a deployment of [1 0;1 1] means the first tier only has access to carrier \(C^{1}\), and the second tier is co-deployed with both carrier bands \(C^{1}\) and \(C^{2}c\). In this simulation set-ups, the first layer is referring to the macrocell layer, and the second is femtocell layer.  
As stated in Section 2, both small scale and large scale fading effects are considered in the channel power gain \(G_{k,m}^{n}\) from (3). The small scale Rayleigh fading effects in \(G_{k,m}^{n}\) is stated by a random variable with zero mean and unit variance, while the large scale fading is assumed by only denoting the path loss effect. A simplified path loss model [38] is built as a function of distance between a user and it serves BS as follows  
\[PL(d_{k,m})=C_{n}(\frac{d_{0}}{d_{k,m}})^{\gamma}. \tag{20}\]  
In this model, the variable \(d_{k,m}\) denotes the distance between user \(m\) and BS \(k\). \(C_{n}\) is a frequency-dependent constant denoted as \(C_{n}=(\lambda_{n}/4\pi d_{0})^{2}\), where \(d_{0}\) is the reference distance for the antenna far field, and \(\lambda_{n}\) is the wavelength of radio carrier \(n\in\mathcal{N}\). And \(\gamma\) is the path-loss exponent depending on propagation environment. Note that considering the scattering environment, this path loss model is valid only when \(d_{k,m}>d_{0}\)[38].  
The other simulation parameters are summarized in Table 1 unless stated otherwise. All of the simulation results are obtained through at least 1000 Monte Carlo channel realizations. For comparison, a stochastic single-flow solution [37] and the traditional max-SINR cell association strategy are considered as benchmarks in the simulation results.
## 2 Pretreatment Methods for Solids Reduction
### Chemical Pretreatment
#### 2.3.2 Ozone Oxidation  
Ozonation is an oxidation pretreatment method that utilizes ozone which is a strong gaseous oxidant and widely applied within drinking water treatment plants to disinfect water. This process has also been used in wastewater treatment processes to oxidize the organic pollutants. The application of ozone disrupts the cell membranes, allowing the increased exposure of released inner cellular material to the AD microorganisms [12]. The reaction mechanism of ozone for organic decomposition is a two-step process: solubilization via the disintegration of suspended solids and the partial degradation of the parent substrate likely to more biodegradable by-products due to the oxidation of the soluble organic matter [12]. Ozone is unstable and thus generated using an ozone generator on-site prior to use which requires energy. Microbubble generation can be applied that provides better mass transfer due to a increased surface area to volume ratio (i.e., bubble to water contact area). Additionally, venture in-line injectors can also be used which simplifies the process footprint and eliminates the need for contact chambers.  
[PERSON] et al. applied 0.015 g O\({}_{3}\)/g TS on waste activated sludge which solubilized 19% and 37% of solids, respectively, and increased the TVS up to 30%. Only partial mineralization occurred which was indicated by 3% decrease in TVS concentrations compared to the original TVS concentration of the feed sludge. When the digestion was continued after another phase of oxidation via the addition of 0.05 g O\({}_{3}\)/g TS, TVS destruction increased to 59% using a 28-day SRT during AD. The overall TVS destruction efficiency improved by 85% to 90% compared to AD alone [46]. Significant energy is required to generate ozone (12.5 kWh/kg O\({}_{3}\)) which could make this pretreatment process costly. This pretreatment method has been applied at the full scale in WWTPs plant with significant biogas production and solids reduction observed [25]. Commercially available ozone pretreatment systems for solids reduction includes Aspal Sludge Inc. who reports improved dewaterability and low energy consumption. Also,Praxair Lyso Inc. reports 80% sludge reduction and improved settling and dewatering characteristics of the treated sludge [25].
# Performance of Concrete with Waste Granite Powder: The Effect of Superplasticizers
## 5 Conclusions  
The experimental program and performance evaluation from different points of view enabled drawing several conclusions related to the use of SP in concrete made with WGP as cement replacement:  
* The workability of concrete is affected by WGP incorporation and greater ratios lead to increasingly greater changes. But using SP efficiently decreases the negative effects of waste on workability;
* 5% WGP content increases the density of concrete, but greater substitution ratios decreases density. The effect of using SP on density is stronger than that of WGP incorporation, but using SP does not change the downward trend of density for replacement ratios over 10%;
* When using SP with less cement content, the results are even much better. Accordingly, using admixtures can be said to be a step towards sustainable development;
* The ER of mix PF40 is 91% and 59% more than that of mixes W40 and W0, respectively. Therefore, using SP can be vital to obtain good electrical properties;
* The effect of WGP incorporation on the resistance to SA is much stronger than that of SP. However, using the two materials together provides the best results;
* Using waste in series W has led to a 0.21 decrease of the ECM index of the mix with 40% replacement in comparison with the mix produced without WGP, while this decrease is about 0.09 and 0.04 for series PT and PF, respectively. The ECM index of mixes PT0 and PF0 increased by 0.12 compared to mix W0. An increase in the replacement ratio did not significantly change the ECM score in series PT and PF, which proves the importance of using SP.  
[PERSON] and [PERSON] designed and performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data and produced the graphs; [PERSON], [PERSON] and [PERSON] wrote the manuscript; [PERSON] and [PERSON] edited the manuscript.  
This research received no external funding.  
The authors declare no conflicts of interest.  
Figure 19: ECM index scores for all concrete mixes.
# Lightweight Detection Method of Obfuscated Landing Sites Based on the AST Structure and Tokens
## 4 Experiment
### Comparison  
Here we compare the proposed system with existing dynamic analysis systems, which are usually employed to analyze obfuscated JavaScript in the real world. Accordingly, we compare the proposed system with the JSUNPACK dynamic analysis system [24] by using 600 obfuscated JavaScript. In Table 9, it is observed that the proposed system requires 0.02 s to process a JavaScript, whereas JSUNPACK requires 27.21 s. The proposed system is therefore four times faster than JSUNPACK. Moreover, the accuracy of the proposed system is 96.8%, which is comparable to JSUNPACK's accuracy.  
Furthermore, we compare our system with a recent static analysis method [17]. Our system requires 0.020 s to process a JavaScript, whereas that in [17] requires 0.024 s. Hence, the proposed system is 20% more efficient than that in [17] in terms of analysis time. However, the accuracy of our system is lower than that of [17], which is enabled by long-time model training and intensive computation for deep learning. The overhead is heavy for maintaining such a system, as training must be occasionally performed to achieve its detection accuracy. Furthermore, when a small number of new obfuscation cases are injected into the system for malicious purposes, the model being trained in [17] is not reflected accordingly because it is based on deep learning, which relies on a sufficiently large dataset to achieve good detection accuracy.  
In contrast, in the proposed system, no training is required; consequently, the maintenance overhead is very low. Even if there is a small number of new obfuscation cases injected into the system, our proposed solution can detect them quickly, unlike the system in [17]. The proposed system enables detection of malicious landing sites in a very short time (within 'golden time'), thereby consuming fewer computational resources with almost the same accuracy as the dynamic analysis system.