esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Therefore, how to make full use of the unlabeled information is a feasible direction for the research of LSM. Semi-supervised learning, a paradigm of machine learning, considers both labeled data and unlabeled data. The core is the assumption that unlabeled samples can provide effective spatial distribution information of features (clustering center estimation) [37]. At present, a great deal of research has been devoted to developing a reasonable framework for training unlabeled data [38; 39]. A weakly labeled support vector machine was proposed to assess urban flood susceptibility [35]. Pseudo-label learning can enhance the training process of models using labeled data and pseudo-labels, and the method has been successfully applied to a variety of classification tasks [30]. Automatic encoder and clustering algorithm were applied to realize pseudo-label classification and pre-training of the network [40]. The efficient and high-quality classifiers were built by simple self-training or cooperative training [32; 41]. As an end-to-end model of semi-supervised learning method, ladder networks can minimize the loss of supervised and unsupervised learning during the training process [42]. The classification accuracy of unlabeled data determines the effect of semi-supervised learning to a certain extent [43]. The cluster-then-label technique for semi-supervised learning [44], as a typical generation model, uses the unsupervised clustering algorithm to identify the clustering of unlabeled data. More recently, a large number of collaborative learning frameworks combined with deep learning and clustering algorithm [45; 46] were constructed to obtain high quality samples and could learn the parameters and depth characteristics of the network iteratively. The results have proven that semi-supervised learning avoids the waste of data and resources, and that problems can be solved such as the weak generalization ability of supervised learning and the imprecision of unsupervised learning [47]. Therefore, in this study, a semi-supervised learning framework based on deep neural network (SSL-DNN) was proposed for LSM, which attempts to pre-train a network using unlabeled samples based on a K-means clustering algorithm, and then conduct supervised fine-tuning using labeled data. We speculate that the SSL-DNN model can achieve more accurate and stable LSM than the traditional supervised learning models. In order to verify the assumptions of the article, a landslide susceptibility evaluation was carried out in Jiaohe County in Jilin Province, China. Meanwhile, SVM and LR models were devoted for comparison. The purpose was that a high-quality LSM can be produced to provide a decision basis for early warning of landslide hazards. In addition, the combination of deep learning and semi-supervised learning was first applied in the field of LSM.
## 3 Results and Discussion
### Purification of the reFAEs  
The vector pET28a used for the expression of reFAEs in _E. coli_ contained a histidine tag (_His-tag_). Thus, the target proteins were isolated and purified by Ni-NTA (Sangon Co., Ltd. (Shanghai, China)) affinity chromatography, His TrapTM HP (0.7 \(\times\) 2.5 cm\({}^{2}\)).  
SDS-PAGE electrophoresis (Figure 3) showed that reFAEs purified by Ni-NTA affinity chromatography obtained a single protein band with a size of 55 kDa, which was consistent with the theoretical relative molecular weights of 55.12 kDa and 55.19 kDa. The CGA hydrolysis activity of the reFAEs was shown in Table 1. Compared with the UmChlE from _U. maydis_ (237.6 U/g) [24], the CGA hydrolysis activity of reFAE2 was increased by 43%.  
Figure 3: SDS-PAGE of purified recombinant proteins. (**Lane M**). Standard protein Marker; (**Lane 1**). GME3292-pET28a (+) cell disruption supernatant; (**Lane 2**). reFAE1 enzyme solution after purification; (**Lane 3**). GME11122-pET28a (+) cell disruption supernatant; (**Lane 4**). reFAE2 enzyme solution after purification.  
Figure 2: Optimization of induction conditions. (**A**) Optimal inducer concentration of reFAEs; (**B**) optimal induction temperature of reFAEs; (**C**) optimal OD\({}_{600}\) value of reFAEs; (**D**) optimal induction duration of reFAEs.  
The relative molecular mass of the reFAEs under non-denaturing conditions was determined with a Superdex 200 10/300 GL gel column. The molecular mass of reFAE1 was about 100-120 kDa, while that of reFAE2 was about 110-120 kDa. Furthermore, SDS-PAGE electrophoresis showed that the relative molecular mass of reFAE1 and reFAE2 was 55 and 57 kDa, respectively. Therefore, it can be speculated that the reFAEs were composed of two homologous monomers. Similar results were found with the FAE in _Aspergillus oryzae_[20].
## 5 Applications of Nanocellulose
### Wastewater Treatment Using Nanocellulose  
The development of treatment methods for wastewater management is of great interest on a global scale, to guarantee the availability of clean water. The release of heavy metals has negative effects on the ecosystem, economy and society. Due to their high aspect ratio and easy accessibility of numerous -OH groups for binding with dyes, heavy metals and other pollutants, nanocellulose excels in this specific application of waste water treatment [113]. Reports have suggested that various heavy metal ions can be extracted using nanocellulose-based material systems. As a result of the availability of numerous reactive groups and highly specific surface areas, the surface modification of nanocellulose improves its binding performance. Different techniques, such as sulfonation, amination and carboxylation are the techniques used to enhance the adsorption efficacy of nanocellulose for heavy metals in wastewater [114]. The effectiveness of rice straw fibers, cellulose fibers and nanocellulose fibers in removing metal ions was examined by [PERSON] et al. [115]. They conducted sorption experiments against Pb(II), Ni(II) and Cd(II) at a metal concentration of 25 mg/L (pH 6). They discovered that at a low dose, nanocellulose fibers outperformed cellulose and rice straw fibers in terms of removal effectiveness. [PERSON] et al. also reported the use of cellulose nanofibers with PVAm grafting to remove Cr (IV) from water [116]. It has also been found that nanocellulose composites have applications in the bioremediation of organic xenobiotic compounds in complex environments [117].
## 3 Semi-Analytical Solution
### Schwartz Iteration Method  
#### 3.2.1 Iterative Methods  
The flow chart of the Schwartz iterative solution is shown in Figure 4.  
Figure 4: Flow chart of the Schwartz iterative method.  
Step 1 of the first iteration: Considering that the hydraulic gradient at the boundary of the parallel edit is \(i_{\text{p}}\) (\(i_{\text{p}}\) is the initial radial hydraulic gradient of the parallel edit boundary) and only the parallel edit is contained in the \(Z_{\text{p}}\) plane, the total head \(h_{\text{p,1}}\) can be obtained using the method described in Section 3.1. The additional radial hydraulic gradient \(i_{\text{t,1}}\) at the tunnel boundary (virtual boundary) can then be determined using \(h_{\text{p,1}}\).  
Step 2 of the first iteration: Considering that the hydraulic gradient at the boundary of the tunnel is \(i_{\text{t}}\)-\(i_{\text{t,1}}\) (\(i_{\text{t}}\) is the initial radial hydraulic gradient of tunnel boundary) and only the tunnel is contained in the \(Z_{\text{t}}\) plane, the total head \(h_{\text{t,1}}\) can also be obtained using the method described in Section 3.1. The additional radial hydraulic gradient \(i_{\text{p,1}}\) at the parallel edit boundary (virtual boundary) can then be determined using \(h_{\text{t,1}}\).  
After the first iteration, the seepage field obtained by summing the total head \(h_{\text{p,1}}\) and \(h_{\text{t,1}}\) fully satisfies the boundary conditions of the tunnel, but an additional radial hydraulic gradient \(i_{\text{p,1}}\) at the parallel edit boundary exists. If \(\mid i_{\text{p,1}}\mid\max\) is less than the required value, the iteration ends; otherwise, the next iteration begins. The iteration termination condition is:  
\[\mid i_{\text{p,k}}\mid_{\max}/i_{\text{p}}<0.1\% \tag{11}\]  
Step 1 of the \(k\)th iteration: Considering that the hydraulic gradient at the boundary of the parallel edit is \(-i_{\text{p,k-1}}\) and only the parallel edit is contained in the \(Z_{\text{p}}\) plane, the total head \(h_{\text{p,k}}\) can be obtained. The additional radial hydraulic gradient \(i_{\text{p,k}}\) at the parallel edit boundary can then be determined using \(h_{\text{t,k}}\). The boundary conditions for the iteration steps are shown in Figure 5. After each iteration, Equation (11) is used to determine if the iteration continues.  
As the iterative process continues, the additional hydraulic gradient around the parallel edit will decay, eventually reaching 0. After the iteration is terminated, the total  
Figure 5: Boundary conditions in alternating iterations.  
water head obtained by each iteration is superimposed to obtain the final water head after \(n\) iterations:  
\[h(x,y)=\sum_{k=1}^{n}[h_{\mathrm{p,k}}(x,y)+h_{\mathrm{t,k}}(x,y)] \tag{12}\]
# Energy Efficient IoT Data Collection in Smart Cities Exploiting D2D Communications
## 4 The Novel D2D-Based Energy Efficient IoT Data Collection
### Clustering for the D2D-Based lot Data Collection  
An important step for the implementation of the proposed D2D-based solution is the clustering of the IoT devices into one or multiple clusters with one aggregator per cluster. We remark that when the channel quality variations both in the uplink towards the eNodeB and among the D2D devices are significant (_i.e._, due to mobility or channel propagation effects) the algorithm can adapt the network configuration to the environmental changes by periodically repeating the solution computation. In particular, the periodicity should be designed depending on how rapidly the environment is changing. Further, based on the cell-mode CQI values for the devices, the solution we propose for the cluster formation problem is an iterative algorithm based on the following simple steps being implemented by the eNodeB:  
1. from the cell-mode CQI list sorted in descending order, select the UEs with highest cell-mode CQI levels as potential aggregators and compute for each of them the number of devices for which a D2D link is feasible;
2. out of the set of potential aggregators, the UE is selected for which the number of devices in coverage for a D2D link is the highest. Given the small data to be sent, any CQI level greater than zero on the D2D link is assumed to be sufficient to send the data;
3. the selected device will act as an aggregator and will form a D2D cluster with the devices in D2D coverage;
4. all devices belonging to the formed cluster are removed from the list; if still devices are present in the ordered list, then repeat the algorithm.  
The iterative algorithm is repeated until all devices are part of a cluster or no new clusters can be formed. The output of the clustering algorithm will define the number and the size of the clusters the IoT devices, and the aggregator for each cluster. Noteworthy, the cluster size could have an influence to the communication efficiency within the cluster itself. Indeed, since a Round Robin scheduling is assumed for the radio resource allocation to the D2D transmitters in each cluster, smaller clusters mean a higher number of RBs available for each D2D transmission. As a consequence, on each D2D link the proposed energy efficient algorithm presented in Section 3.2 may introduce higher benefits. The motivation for this is that the algorithm is implemented over a larger number of RBs and has higher margins to optimize the energy and efficiency in the communication. On the contrary, if the cluster size is big, the opposite observations can be made. In particular, the extreme case is represented when only 1 RB per D2D link is available, where the benefits introduced by the algorithm are related exclusively to the use of a more robust MCS (_i.e._, without decreasing the number of RBs).
## 5 Effective Linewidth  
We start with the variance of the phase difference between two consecutive symbols [25]:  
\[\phi(t)-\phi(t-T)=2\pi\int_{t-T}^{t}v(t^{\prime})dt^{\prime}=2\pi\int_{-\infty} ^{\infty}v(t^{\prime})h(t-t^{\prime})dt^{\prime}=2\pi v(t)*h(t) \tag{3}\]  
where \(h(t)=1\) for \(0<t<T\) and \(T\) is the symbol period. The phase variance is then:  
\[\sigma_{\Delta\phi}^{2}=\int_{-\infty}^{\infty}S_{\Delta\phi}(f)df=4\pi^{2} \int_{0}^{\infty}\left|H(f)\right|^{2}S_{FM}(f)df=4\pi^{2}T^{2}\int_{0}^{ \infty}\left|\frac{\sin\pi fT}{\pi fT}\right|^{2}S_{FM}(f)df \tag{4}\]  
We then define the Effective Linewidth as:  
\[\Delta v_{eff}=2\pi NT\int_{f_{cons}}^{f_{s}/2}\left|\frac{\sin\pi fNT}{\pi fNT }\right|^{2}S_{FM}(f)df \tag{5}\]  
where \(f_{cross}\) is the frequency at which \(1/f\) noise crosses the level of intrinsic linewidth \(\Delta v_{int}\), and \(f_{s}\) is the sampling rate of the analog to digital converter (ADC). The variable \(N\) is the number of symbols used by the CPR algorithm for phase averaging. This is introduced because a CPR algorithm (e.g., BPS) averaging over N symbols (taps), will effectively act as a low pass filter. Therefore, \(1/NT\) is the bandwidth seen by the digital coherent receiver using \(N\) taps for the BPS filter length. If a decision directed phase lock loop (DDPLL) was used instead, \(1/NT\) should be replaced by the equivalent  
Figure 8: Experimental results for transmission when phase noise comes from local oscillator (LO) laser.  
feedback loop bandwidth. Equation (5) basically represents the area under the curve determined by the FM PSD, up to the aforementioned bandwidth, and excluding \(1/f\) noise.  
Figure 9 shows the calculated effective linewidth for the system described in Section 4 where the CPR algorithm assumes 17 taps. The left and right figures illustrate how the effective linewidth was calculated on experimental data. In all cases, a lower cut-off frequency of 40 MHz was used to avoid integrating over \(1/f\) noise, an issue that requires further investigations. We can again observe the same tendencies as in Figure 7. When \(K\) and \(F_{r}\) are highest and the frequency noise is almost flat, the effective linewidth is very close to the 3 dB linewidth. However, when the resonance peak is within the integrating area, for \(\Delta v_{int}=500\) kHz, the effective linewidth rises to 20.6 MHz, which explains the high BERs obtained in Figure 7. Figure 10 shows penalty _vs_. linewidth and transmitter laser effective linewidth on both simulated and experimental data from Sections 3 and 4 respectively. In both cases the induced phase noise is at the transmitter laser. We can clearly observe a strong correlation of penalty _vs_. effective linewidth, while this is not the case when comparing to the 3 dB linewidth.  
Figure 10: Comparison of system penalty _vs_. 3 dB linewidth (**left**) and effective linewidth (**right**).  
Figure 9: Calculations of effective linewidth on experimental data for 17 carrier-phase recovery (CPR) taps.
# Lightweight Design of Multi-Objective Topology for a Large-Aperture Space Mirror
## 2 Lightweight Design of the Primary Mirror  
### Fundamental Concepts  
Topology optimization is to find the best material distribution or force transmission path in a given design space, so that the lightweight design can be obtained under various constraints. Topology optimization of the primary mirror structure based on solid isotropic material with penalization (SIMP) was applied in this study using the software OptiStruct [11]. Its basic idea is to assume a material element that has variable relative density \(\rho\) between 0 and 1. When \(\rho\) is 1 or near 1, it means the element is required and need to be retained; when \(\rho\) is 0 or near 0, it says the element is not filled with material and needs to be removed. In order to avoid too many middle density elements, introducing penalty factor to enable continuous variables approach to 0 or 1 approximately, and the material properties of the element can be defined by  
\[E_{i}=(\rho_{0}+(\rho_{1}-\rho_{0})\rho_{i}^{P})E_{0}, \tag{1}\]  
where \(\rho_{i}\) is the material relative density and \(E_{i}\) is the relative elasticity modulus of the \(i\)-th element, respectively. In the commercial software OptiStruct, the lower bound \(\rho_{0}\) takes the value of 0.01, that is to avoid the singularity of stiffness matrix in the process of finite element analysis, and the upper bound \(\rho_{1}\) takes the value of 1. \(E_{0}\) is the inherent elasticity module of a given isotropic material, and \(P\) is the penalization power.
# Uncertainty Analysis and Improvement of Propellant Gauging System Applied in Space
## 3 Measuring Uncertainty Analysis and the Modified Model
### Modified Model  
According to the results in Figure 2, the temperature and pressure measurement in the tank showed an important effect on the final gauge uncertainty. There seems to be a great need to improve the precision of the sensors. Then, the final measuring uncertainty can be reduced by adjusting the measuring precision in the tank. An adjustment schematic was created, and the measuring precision of the pressure and temperature was 0.04% FS and 0.35% FS.  
The final mass gauge uncertainties of the adjustment schematic are shown in Figure 3.  
The measuring uncertainty is improved by using better temperature and pressure measurements, as shown in Figure 3. When the temperature measuring accuracy in the tank is within 0.4% FS, the final measuring uncertainty can reach 0.4% FS. When the pressure measuring accuracy in the tank is within 0.04% FS, the final measuring uncertainty can  
Figure 2: Effect of each factor on the final mass gauge uncertainty ((**a**) is of the total volume of the tank, (**b**) is of the injection bottle volume, (**c**) is of the initial pressure of the injection bottle, (**d**) is of the initial temperature of the injection bottle, (**e**) is of the final pressure of the injection bottle, (**f**) is of the final temperature of the injection bottle, (**g**) is of the final pressure of the tank, (**h**) is of the final temperature of the tank, (**i**) is of the initial pressure of the tank, and (**j**) is of the final temperature of the injection bottle).  
be improved to 0.008% FS. If the temperature accuracy is improved to 0.14% FS, the final uncertainty can be controlled to be within 0.1% FS. The results of the simulated calculation can be seen in Figure 4.  
Figure 4: Effect of the temperature accuracy in the tank on the final measuring uncertainty ((**a**) is of the initial temperature of the tank, and (**b**) is of the initial temperature of the tank).  
Figure 3: Effect of the improved measuring uncertainties in the tank on the final measuring uncertainty((**a**) is of the initial pressure of the tank, (**b**) is of the initial temperature of the tank, (**c**) is of the final pressure of the tank, and (**d**) is of the final temperature of the tank).  
Based on the temperature and pressure range in the application of the PGS method, the pressure range is suggested to be 0-1 MPa, with an uncertainty of 0.4 kPa. The temperature range is suggested to be 18-23 \({}^{\circ}\)C, with an uncertainty of 0.4 K.
# Comprehensive Review of Traffic Modeling: Towards Autonomous Vehicles
## 5 Traffic Flow Models
### Microscopic Models
#### 5.2.5 Lane-Changing Models
[PERSON] and [PERSON] [195] developed a classification scheme for driver types, which aimed to predict the likelihood of a driver attempting a discretionary lane change based on their background information. Their work also identified the factors and parameters that influence whether a driver executes a particular lane-changing maneuver, depending on whether it is mandatory or discretionary. The application of game theory to lane-changing behavior has provided further insights into the strategic interactions between drivers. [PERSON] [196] and [PERSON] et al. [197] were among the first to apply game theory to model merging behavior, highlighting the competitive and cooperative strategies that drivers might use. Building on this, [PERSON] et al. [198] developed a lane-changing control approach for connected and automated vehicles that incorporates both cooperative and non-cooperative game theory formulations. [PERSON] et al. [172] compared a lane-changing strategy based on game theory with the traditional STCA (symmetric two-lane cellular automaton) strategy, demonstrating the potential benefits of game-theoretic approaches in improving traffic flow and safety. In summary, the evolution of lane-changing models reflects a progression from simple, rule-based frameworks to more complex and integrated approaches that incorporate elements of utility theory, artificial intelligence, and game theory. These models have significantly advanced our understanding of how lane-changing decisions are made and how they impact traffic flow, particularly in increasingly automated and connected traffic environments. The relationships between these studies illustrate the continuous refinement of lane-changing models, with each new development building on and extending the capabilities of previous models.
## 7 Technical Considerations for Implementing 6G Technology
### Intelligent Reflecting Surfaces (IRS) and \"Mirrors\"  
To mitigate signal blockages and extend coverage, 6G networks will leverage intelligent reflecting surfaces (IRSs), often referred to as \"mirrors\":  
* Reconfigurable Reflectors: IRSs consist of passive or semi-passive elements that can dynamically adjust their reflective properties to redirect and focus THz signals toward users. This technology is critical for maintaining connectivity in environments with obstructions or when LOS paths are unavailable [262].
* Beam Steering and Power Efficiency: By controlling the phase and amplitude of reflected waves, IRSs can steer beams and improve power efficiency, enabling better energy usage in densely populated urban areas [263].
* Enhancing Spectral Efficiency: IRS technology improves spectral efficiency by dynamically optimizing channel conditions, reducing interference, and boosting overall throughput [263].  
Notably, 6G technology aims to integrate these advancements into a unified framework that supports applications like extended reality (XR), digital twins, and the tactile internet. By addressing propagation challenges, optimizing cell architecture, and deploying IRS, 6G networks will achieve unparalleled connectivity, speed, and reliability.