esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Investigating Several RL-Based Routing Methods
### [PERSON] et al. in [67] have proposed the reinforcement learning-based hybrid routing algorithm (RHR) for VANETs. It is a multipath routing method, which combines proactive and reactive routing techniques. Multipath routing increases fault tolerance in RHR because if a route fails for any reason, the next path will be replaced. Moreover, RHR can solve the blind path problem. This problem means that a route will be invalid before ending its route expiration time. Lind path increases packet loss rate in the network. RHR can solve this problem because it utilizes multiple paths between two nodes. This method uses a reinforcement learning algorithm to make a decision on the path. Additionally, there are \(k\) nodes as the next-hop node (if exist) for each route to a particular destination in the routing table. This means that RHR can simultaneously discover multiple routes between source and destination. It maintains information such as the route weight and the route lifetime for each entry in the routing table. The route weight indicates the priority of a path for transferring data and is determined by Q-learning algorithm. In RHR, the Q-learning technique implements the routing operation. In this scheme, the next-hop node selection modes are considered as a set of states. Furthermore, the action set corresponds to the reception of different packets related to the next-hop vehicle. Figure 22 illustrates the learning structure of RHR. Taking different actions has a different impact on the routing table and changes the weight of each path according to this selected action. This weight is used as feedback to determine how much the path is suitable for transferring data. In RHR, selecting the next-hop node is limited to \(k\) nodes to manage the size of Q-table. In this table, Q-value is refreshed according to the information of the exchanged packets to gradually converge to the best path, so that paths with higher weight will be recorded in the routing table and weak paths will be eliminated. RHR designs the broadcast control technique to prevent routing loops. For example, it adjusts the time interval for broadcasting beacon messages dynamically. According to this adaptive broadcast technique, this time interval is determined by a vehicle position prediction strategy. This strategy uses the speed information of vehicles and reduces bandwidth consumption when broadcasting beacon messages. On the other hand, a vehicle broadcasts its data packets when it has no path to the desired node. To control the broadcast process, it determines a time to live (TTL) for this packet to manage the flooding problem. If the desired vehicle is the neighbor of the transmitter node, the packet is sent in a unicast manner. Also, if the number of neighbors is very high, the vehicle will stop the broadcast process to reduce the flooding problem.  
Figure 21: Learning structure in [PERSON] et al. scheme.
## 4 Radiometric Processing Workflow
### Scene Reflectance Generation  
The instrument recordings can be transformed to reflectance either by using information of the incident irradiance or utilizing reflectance reference targets on the ground and an empirical line method (ELM).
# A Novel FECAM-iTransformer Algorithm for Assisting INS/GNSS Navigation System during GNSS Outages
## 2 Theory of INS/GNSS Integrated Navigation System
(2022) [PERSON]; [PERSON]; [PERSON] A Novel Method for AI-Assisted INS/GNSS Navigation System Based on CNN-GRU and CKF during GNSS Outage. _Remote Sens._**2022**, _14_, 4494. [CrossRef]
* [PERSON] (1932) [PERSON] A New Approach to Linear Filtering and Prediction Problems. In _Control Theory: Twenty-Five Seminal Papers (1932-1981)_; IEEE: Piscataway, NJ, USA, 2001; pp. 167-179. [CrossRef]
* [PERSON] et al. (2007) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Vision-aided inertial navigation for pin-point landing using observations of mapped landmarks. _J. Field Robot._**2007**, _24_, 357-378. [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Dual-optimization for a MEMS-INS/GPS system during GPS outages based on the cubature Kalman filter and neural networks. _Mech. Syst. Signal Process._**2019**, _133_, 106222. [CrossRef]

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# MSEN: A Multi-Scale Evolutionary Network for Modeling the Evolution of Temporal Knowledge Graphs
## 6 Discussion  
The experimental results demonstrate that the proposed MSEN model achieves superior performance compared to several baselines on typical benchmark datasets. The better results validate the effectiveness of modeling multi-scale evolutionary information for predicting future events in TKGs.  
The ablation studies in Section 5.2 provide insights into the contribution of each module in MSEN. Removing the global memory encoder leads to a significant performance drop, showing the importance of capturing long-distance temporal dependencies. The local memory encoder also contributes steady improvements by aggregating rich semantics within each timestamp. Using HT-GNN and TR-GNN as the graph neural network encoders also leads to better results compared to alternative GNN models, demonstrating their capabilities in encoding structural and temporal patterns from TKGs.  
The multi-scale modeling in MSEN aligns with findings from previous works such as TiRGN [14] which show combining local and global patterns is beneficial. However, MSEN proposes new designs for each module, leading to better results. The hierarchical design in HT-GNN enriches semantic learning, while the periodic and aperiodic temporal modeling in TR-GNN improves temporal encoding.
## 3 Experimental Results and Analysis
### Experimental Dataset  
In this paper, we use two sets of dataset published in Ref [23]. The data was collected in Tianjin, China. The density of the test region point cloud is about \(20\,\sim 30/\text{m}^{2}\). The data set contains both large objects (buildings and trees) and small objects (cars). It contains different roof shapes, buildings of different heights, and dense and overlapping cars and trees. Table 1 lists the number of each class point for the two scenes. Figure 4a,b shows the training data of scene 1 and scene 2, respectively.  
All the programs are run on a computer with an Intel Core i7-7700K CPU, 4.20 GHz with 24-GB RAM. The algorithm is implemented on the C++ platform based on PCL 1.8.0. Each set of data training and testing takes about 6.5 min. However, the feature extraction and optimization process can be implemented in parallel. Therefore, the efficiency of the proposed method can be further improved, and the speed of the point cloud processing will be greatly improved.
## 1 Introduction
### Line Coding  
Run-length limited (RLL) coding is a line-coding technique that is used to send arbitrary data over a communications channel that has bandwidth limits. RLL codes are defined by four main parameters: \(m\), \(n\), \(d\), and \(k\). The first two, \(m\) and \(n\), refer to the rate of the code, while the remaining two specify the minimum (\(d\)) and maximum (\(k\)) number of zeros between consecutive ones. Particular RLL codes are used in light communication to provide DC-balance since flicker mitigation is required in VLC (except in some OCC modes). With the proposed scheme, we recommend three types of line coding: Manchester code, 4B6B, and 8B10B, which also proposed in IEEE 802.15.7-2018 standard [6].
## 2 Silicon Photonics Micro-Transceiver with Multimode Interface
### Optical Transmitter Signal Integrity Measurements  
Figure 13 shows the optical eye diagrams from the four output channels driven at 25.78 Gbps. The eye diagrams in the left column were recorded at an ambient temperature of 25 \({}^{\circ}\)C, while the eye diagrams in the right-hand column were recorded at an ambient temperature of 105 \({}^{\circ}\)C. The IOCore micro-transceiver does not include a clock and data recovery (CDR) circuit; nonetheless, a negligible difference in signal integrity and low jitter were recorded across the temperature range.  
Transceivers with data rates of 25 and 32 Gbps have been reported [3; 19], and a 50 Gbps version is currently under development with demonstration planned in 2022.  
Figure 11: Mach–Zehnder modulator (MZM) design and performance. (**a**) Schematic diagram of MZM driven by CMOS IC; (**b**) frequency characteristics of the MZM.  
Figure 12: Distribution of optical modulation amplitude as measured on the outputs of the IOCore Tx optical pins.
## 5 Discussion
### Comprehensive Discussion of Model Performance
The coefficient of determination (R\({}^{2}\)) of the SARIMA model shows that it fits the data relatively well, which means that the model can explain the variation of the data to a certain extent. However, in some months, its error indicators are still high, indicating that there is room for improvement in its prediction accuracy. In summary, the LightGBM model has advantages in capturing complex nonlinear relationships, but its stability is poor. The Prophet model has methods to deal with seasonality and trends, but it lacks adaptability to complex changes and is easily affected by special circumstances. The SARIMA model has a theoretical basis for time series prediction, but it has limitations in dealing with non-stationary data and spatial heterogeneity.
# Throughput Capacity of Selfish Wireless _Ad Hoc_ Networks with General Node Density
## 3 Achievable Rate
### The Rate for Transporting a Packet
Using Theorem 3, we can conclude that an achievable rate along the horizontal highways is  
\[R^{Hh}= \begin{cases}\Omega\left(l^{-\alpha}(n)\left(\frac{p(n)}{n}\right)^{1/ 2}\right)&\text{if }l(n)=\Omega(1)\\ \Omega\left(\left(\frac{p(n)}{n}\right)^{1/2}\right)&\text{if }l(n)=O(1)\end{cases}  \tag{15}\]  
Similarly, the rate on vertical highways is  
\[R^{Hv}= \begin{cases}\Omega\left(l^{-\alpha}(n)\left(\frac{p(n)}{n}\right)^{1/ 2}\right)&\text{if }l(n)=\Omega(1)\\ \Omega\left(\left(\frac{p(n)}{n}\right)^{1/2}\right)&\text{if }l(n)=O(1)\end{cases}  \tag{16}\]  
Combining Equations (15) and (16), we finish the proof of Lemma 6. The following lemma illustrates the achievable rate of the delivery phase. **Lemma 7**: _The receiver can attain a rate of \(R^{Dl}\) from an exit point on the highway, where \(R^{Dl}\) is_  
\[R^{Dl}= \begin{cases}\Omega\left(p(n)\left(\frac{\log^{2}np(n)}{\sqrt{\zeta p( n)}}\right)^{\alpha}\right)&\text{if }l(n)=\Omega(\log np(n))\\ \Omega\left(\frac{p(n)}{\log^{2}np(n)}\right)&\text{if }l(n)=O(\log np(n)) \end{cases}  \tag{17}\]  
**Proof. ** The delivery phase is an opposite process to the draining phase, while the transmission is from highways to the destination. Combining the Lemma 5, Lemma 6 and Lemma 7, we can derive the rate bottleneck of SWANETs  
**Theorem 8**: _Comparing the achievable rate of each phase, we get the rate of per-node in SWANETs with general nodes density is_  
\[T(n)= \begin{cases}\Omega\left(\frac{1}{\sqrt{n}}p^{\frac{1}{2}}(n)(\zeta p(n) )^{\alpha}\right)&\text{if }l(n)=\Omega(1)\\ \Omega\left(\frac{1}{\sqrt{n}}p^{\frac{1}{2}}(n)\right)&\text{if }l(n)=O(1)\end{cases}  \tag{18}\]
# Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme for Heterogenous Vehicular Network +
## 3 Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme
### Gateway Discovery Process
#### 3.3.3 Distance between the IV and NV  
Another parameter used by the IV in selecting the best MGW is the distance to each NV. Using the _LOC_ of the IV inserted in the GWreq message, the inter-separation distance \(D_{IVNV}\) is computed using Equation (7). Then, the NV inserts the computed \(D_{IVNV}\) into the GWrep message. Suppose the IV and NV are located at the GPS coordinator \(\left(x_{i},y_{i}\right)\) and \(\left(j_{i},y_{j}\right)\), respectively (refer to Figure 4).  
The \(D_{IVNV}\) is computed as follows.  
\[D_{IVNV}=\sqrt{\left(x_{i}-x_{j}\right)^{2}+\left(y_{i}-y_{j}\right)^{2}} \tag{7}\]  
where \(\left(x_{i},y_{i}\right)\) and \(\left(x_{j},y_{j}\right)\) are GPS coordinates of the IV and NV, respectively.
## 4 Role of DAPG-Producing _Pseudomonas_ spp. in Natural Soil Suppressiveness
### Natural Soil Suppressiveness to Tobacco Black Root Rot  
Several morainic soils located in the Swiss region of Morens are naturally suppressive to tobacco black root rot [88; 89], a disease caused by the fungus _Thielaviopsis basicola_. By contrast with take-all decline, which is induced by monocultures of wheat and barley, suppressiveness to tobacco black root rot can be found in soils with different cropping history (monoculture or crop rotation) [89]. The natural suppressiveness found in the Morens soils can be eliminated by heat treatment [88], demonstrating its microbial origin. Several DAPG-producing _Pseudomonas_ strains were isolated from these suppressive soils, including the model strain _P. protegens_ CHA0 [88]. When inoculated, strain CHA0 was able to control tobacco black root rot in 36 out of 39 conducive soils [88]. DAPG-producing _Pseudomonas_ spp. were found to reach high population densities in the rhizosphere of tobacco plants grown in suppressive soils [90; 91]. However, they were detected at similar population levels in conducive soils [90; 92]. This suggests that, contrary to take-all decline, disease suppression does not originate from the buildup of DAPG-producing _Pseudomonas_ spp. populations in the rhizosphere. Nevertheless, several genotypes of DAPG-producing _Pseudomonas_ spp. were exclusively found in the suppressive soils [90; 91], suggesting that differences in the population structure of DAPG-producing _Pseudomonas_ spp. might explain the differences in suppressiveness between conducive and suppressive soils. However, this conflicts with the finding that DAPG-producing _Pseudomonas_ strains isolated from conducive soils protected tobacco roots to a similar extent as isolates from the suppressive soils did [93]. The conducive and suppressive soils of the Morens region have different geological origins and compositions. Indeed, vermiculite is the predominant clay mineral in the morainic suppressive soils, while illite is the predominant one in conducive soils. Several studies have demonstrated that DAPG-producing _Pseudomonas_ spp. protected tobacco roots to a better extent in vermiculite soils [94; 95]. Indeed, the presence of vermiculite instead of illite is associated with a higher iron availability and a higher _phLA_ expression [95]. Lastly, other plant-beneficial microbial strains may be involved and act in concert with DAPG-producing _Pseudomonas_ spp. to control tobacco root rot [89; 96].
###### Abstract  
This paper aims to introduce a novel maximum power point tracking (MPPT) strategy called transfer reinforcement learning (TRL), associated with space decomposition for Photovoltaic (PV) systems under partial shading conditions (PSC). The space decomposition is used for constructing a hierarchical searching space of the control variable, thus the ability of the global search of TRL can be effectively increased. In order to satisfy a real-time MPPT with an ultra-short control cycle, the knowledge transfer is introduced to dramatically accelerate the searching speed of TRL through transferring the optimal knowledge matrices of the previous optimization tasks to a new optimization task. Four case studies are conducted to investigate the advantages of TRL compared with those of traditional incremental conductance (INC) and five other conventional meta-heuristic algorithms. The case studies include a start-up test, step change in solar irradiation with constant temperature, stepwise change in both temperature and solar irradiation, and a daily site profile of temperature and solar irradiation in Hong Kong.  
Keywords:photovoltaic systems; MPPT; partial shading condition; transfer reinforcement learning; space decomposition +
Footnote †: journal: Applied Soft. 2019, 9, 2769; doi:10.3390/appr9132769
## 2 Experimental Investigation
### Experimental Setup  
This experimental work involved the preparation of prismatic mortar test specimens measuring 6 \(\times\) 8 \(\times\) 2 cm\({}^{3}\), in which 3 parallel steel rebars with a diameter of 6 mm were embedded (Figure 1). The study is a continuation of research into LFS as a partial replacement for aggregate and cement in mortars [32; 33] from the viewpoint of its behavior with regard to rebar corrosion and the symptom this produces with the time.  
All the steel bars were measured, numbered, and weighed before their introduction into the mold. The steel-concrete-atmosphere interface was protected by adhesive tape over a length of 5 cm in order to avoid the possibility of localized corrosion attacks by differential aeration, leaving an effective length of 6 cm contact between the steel and the concrete. The mortar was kneaded in a mechanical mixer for 90 s. The mold was filled in two pourings, coinciding with the arrangement of the steel bars. Thereafter, test specimens were placed in a humidity chamber for 24 h. After this period, the specimens were demolded and placed again in the humidity chamber for 28 days, and then they were left to dry normally. With the aim of studying the corrosion of the bars by using electrochemical techniques, the specimens were once again placed in a humidity chamber for 550 days, at a temperature of 20 \({}^{\circ}\)C and a relative humidity 95%, associating each measurement of corrosion potential and corrosion rate with the degree of moistness of the mortar.  
To highlight the symptoms that rebar corrosion produces in the mortar test specimens with mixed-in chlorides, the corrosion process was further accelerated by connecting the central bar of the standard test specimens as well as the test specimens containing LFS, both having mixed-in chloride ions percentages of 1.2% and 2%, to a direct current source. Once connected, a constant anodic current of 1.3 mA, corresponding to 10 \(\mu\)A/cm\({}^{2}\), was passed through each central bar for 282 days. During the periodic measurements of the voltage needed to maintain this preset current and observation of the symptoms, the degree of moistness of the test specimens, with measurements taken before and after their moistening on each occasion, was again associated with the stabilized potential.
## 5 Discussion and Conclusions  
High renewable penetration could lead to a significant reduction of the system's cost, while hydro-pumped storage systems may contribute to the peak demand supply.  
The size of the reverse hydro units should be proportional to the development of Wind and PV installed capacity. The over-dimensioning of reverse hydroelectric projects may lead to installed hydroelectric pumps and turbines which will be used only for a few hours per year without ensuring their economic feasibility, while under-dimensioning will lead to insufficient exploitation of the potential. Furthermore, the higher integration of renewable sources (wind and PV) across the country (spatial dispersion) results in significant energy surplus and curtailment during the year, therefore energy that could be stored in hydro-pumped storage systems is increased. In this case, hydro-pumped storage units can contribute significantly to the energy balance, as analyzed in the scenarios.  
The economic assessment of the future power system of Greece, based on the scenarios examined in this connection, demonstrates that the increase of RES contribution may lead to lower costs for the system. Greater penetration of photovoltaic and wind installations results in a reduction of the levelized cost of energy of the system, since capital expenditures required for wind and PV systems are expected to decrease significantly over the years. The cost of thermal units in reference years 2030 and 2050 is expected to be higher due to the increase in the cost of carbon dioxide emission allowances. The reduction of the energy  
Figure 7: Levelized Cost of Energy and contribution to the weighted average cost of the system by technology (the size of the bubbles corresponds to power generation by each type of technology).  
production share of the latter in combination with the higher integration of RES prevents the increase of the weighted average LCOE of the power system.  
Conceptualization, G.C. and A.Z.; methodology, G.C. and A.D.; software and formal analysis, A.D.; investigation, A.D.; data curation, V.K. and K.L.; writing--original draft preparation, A.D., T.C.; writing--review and editing, A.D. and G.C.; supervision, G.C.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Staggered BPMR Channel Model and PRML Detection  
### Staggered BPMR Channel Model  
Figure 2 is a block diagram of the proposed system model. Before passing through the staggered BPMR channel, the binary user data \(a_{k}\in[0,1]\) are encoded by a modulation encoder to encode the 2D data array \(c_{p,q}\), and \(c_{p,q}\) is magnetized to record data \(d_{p,q}\in\{-1,1\}\). The analytical 2D Gaussian island pulse response \(P\left(z,x\right)\) without media noise and write errors is given in [14].  
\[P(z,x)=A\cdot exp\left\{-\frac{1}{2c^{2}}\left[\left(\frac{z}{PW_{z}}\right)^{ 2}+\left(\frac{x}{PW_{x}}\right)^{2}\right]\right\}, \tag{1}\]  
where \(z\) and \(x\) are the indices in the cross- and down-track directions, respectively, \(A\) is the normalized peak amplitude, \(c\) represents the relationship between the standard deviation of a Gaussian function and PW50 (a parameter of the pulse width at half of the peak amplitude), and \(PW_{z}\) and \(PW_{x}\) are the PW50 of the cross- and down-track pulses, respectively. In this study, \(A=1,c=1/2.3548,PW_{z}=24.8\) nm, and \(PW_{x}=19.4\) nm. The BPMR 2D channel island pulse response \(h_{m,n}\) is calculated by sampling the 2D Gaussian island pulse response as follows:  
\[h_{m,n}=P(mT_{z}+\Delta_{TMR},nT_{x}), \tag{2}\]  
where \(m\) and \(n\) are the indices of bit islands for the cross- and down-track directions, respectively, \(T_{z}\) and \(T_{x}\) denote track pitch and bit period, respectively, and \(\Delta_{TMR}\) is the read head offset, which is  
Figure 1: Bit-patterned media (BPM) structures of regular and staggered array.  
generated when the recording heads cannot remain at the center of the main data track. The \(\Delta_{TMR}\) is expressed as follows:  
\[\Delta_{TMR}=\frac{TMR_{z}\times T_{z}}{100}, \tag{3}\]  
where \(TMR_{z}\) is the percentage of the TMR. The readback signal \(r_{p,q}\) corrupted by electronic noise in staggered array BPMR is given by  
\[\begin{array}{l}r_{p,q}=\sum_{n=-N}^{N}d_{p,q+n}\cdot h_{0,n}+\sum_{m=0}^{ \lfloor\frac{N-1}{2}\rfloor}\sum_{n=-N+m+1}^{N-m}d_{p-(2m+1),q+n}\cdot h_{-(2m+1 ),n-\frac{1}{2}}+\\ \sum_{m=0}^{\lfloor\frac{N-1}{2}\rfloor}\sum_{n=-N+m+1}^{N-m}d_{p+(2m+1),q+n} \cdot h_{(2m+1),n-\frac{1}{2}}+\sum_{n=-1}^{\lfloor\frac{N}{2}\rfloor}\sum_{n=- N+m}^{N-m}d_{p-2m,q+n}\cdot h_{-2m,n}+\\ \sum_{m=1}^{\lfloor\frac{N}{2}\rfloor}\sum_{n=-N+m}^{N-m}d_{p+2m,q+n}\cdot h_{ 2m,n}+n_{p,q}\end{array} \tag{4}\]  
where \(N\) is the length of interference from neighboring islands, \(\lfloor x\rfloor\) is a floor function, which is the function that takes as input a real number \(x\) and gives as output the greatest integer less than or equal to \(x\), and \(n_{p,q}\) is electronic noise modeled as additive white Gaussian noise with variance \(\sigma^{2}\) and zero mean. Since the interference from neighboring islands in \(N=2\) is relatively negligible, we set \(N=1\) for simplicity.