esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Discussion of the Overall Reconstruction Error and the Hotspot Tracking Performance
In contrast to AHK, AGES gives local refinement of grids near identified hotspots, as shown in Figure 10. Consequently, the hotspot grids in the final mesh configurations feature are significantly smaller than the initial uniform grids, and the hotspot temperature values are corrected by averaging the refined grids in its vicinity, leading to a substantial reduction of \(E_{\text{h}}\). Then, the AGES-AHK method combines the advantages of AGES and AHK to achieve the best hotspot temperature tracking performance with \(E_{\text{h}}\) values considerably lower than those of the basis method. At \(\text{coef}_{\text{HE}}\) values of 0.1, 0.2, and 0.5, the maximum \(E_{\text{h}}\) of _Tfield1_, _Tfield2_, and _Tfield3_ does not exceed 2.5%, 5% and 15%, respectively. Even at the highest \(\text{coef}_{\text{HE}}\) value of 1, the maximum \(E_{\text{h}}\) of the three temperature phantoms is less than 25%, significantly lower than the basis method. Another important criterion for hotspot tracking performance is the hotspot position deviation \(D_{\text{h}}\). As shown in Table 3, _Tfield1_ exhibits \(D_{\text{h}}=0\) across all 12 cases due to its inherent high symmetry. Similarly, Table 4 reveals that _Tfield2_ maintains \(D_{\text{h}}<1.41\%\) at \(\text{coef}_{\text{HE}}=0.1\) and 0.2, with \(D_{\text{h}}\) collapsing to 0 at \(\text{coef}_{\text{HE}}=0.5\) and 1, a consequence of its symmetric configuration. Critically, the proposed AGES and AHK methods fail to achieve meaningful \(D_{\text{h}}\) reduction in either _Tfield1_ or _Tfield2_. This limitation arises because the intrinsic symmetry of these phantoms inherently minimizes localization errors, leaving minimal margin for algorithmic improvement. In contrast, the highly asymmetric temperature phantom _Tfield3_ exhibits markedly divergent responses to AGES and AHK methods regarding \(D_{\text{h}}\), as quantitatively validated in Table 5. The AHK method fails to meaningfully reduce \(D_{\text{h}}\) because AHK does not alter the fundamental uniform domain discretization scheme. Consequently, the inherent limitation of the domain discretization assumption--which obscures continuous temperature variations within individual grids--remains unaddressed. Conversely, AGES demonstratessuperior \(D_{\rm h}\) performance than that of the basis method and the AHK method. This is because AGES is iterated with the grid in which the hotspot is located. The hotspot grids in the final mesh configurations feature are significantly smaller than the initial uniform grids. Then hotspot positions are refined via temperature gradient analysis between nonuniform hotspot grids and adjacent regions, which leads to a decrease in \(D_{\rm h}\). The finalized AGES-AHK method integrates the benefits of AGES and AHK. In all three temperature phantoms, the AGES-AHK method achieves an average \(D_{\rm h}\) of less than 2.14% and a maximum \(D_{\rm h}\) of less than 3%, representing the optimal performance in hotspot position tracking, as evidenced by Table 5.
###### Abstract  
Yield and stability are desirable characteristics that crops need to have high agronomic value; sugarcane stands out globally due to its diverse range of products and by-products. However, genotype-environment (G \(\times\) E) interactions can affect the overall performance of a crop. The objective of this study is to identify genotypes with the highest yield and stability, as well as to understand their independent and interactive effects. A collection of 10 sugarcane varieties was evaluated, including Colombian, Dominican, Ecuadorian lines, and a group of clones planted across five different locations from 2018 to 2020. A two-way ANOVA along with the GGE biplot technique were used to analyze yield and stability. The ANOVA model shows highly significant effects in all cases (\(p<0.001\)) except for the genotype by year and sector interaction (G \(\times\) Y \(\times\) S); however, the decomposition by sectors reveals a significant triple interaction in sector 04 (\(p<0.05\)). The GGE biplot model accounted for up to 74.77% of the total variance explained in its PC1 and PC2 components. It also highlighted the group of clones as having the highest yield and environmental instability, and the Ecuadorian varieties EC-07 and EC-08 as having the best yield-stability relationship. We conclude that the combined results of the ANOVA and GGE biplot models provide a more synergistic and effective evaluation of sugarcane varieties, offering theoretical and practical bases for decision-making in the selection of specific varieties.  
yield; stability; sugarcane; interaction; GGE; biplot Article
# Tracking System for a Coal Mine Drilling Robot for Low-Illumination Environments
## 3 The Proposed Method
### Long-Term Memory Module  
In practical applications, trackers often operate for a long time. Objects outside the field of view and occlusions are the main problems in long-term tracking because a target typically does not reappear in the same position where it disappeared. Coal mines have few and uneven light sources, resulting in low illumination. When the scene changes from a dark to a light environment, the camera is suddenly exposed to the light source, which represents a crucial problem that has to be solved in long-term tracking. Therefore, the tracker needs to detect the location of the target in the new scene and relate it to the sample model in the previous scene to ensure consistent tracking.  
The Long-Term Memory Module has two parts: the long-term filter \(\mathbf{f}_{\text{Long}}\) and the long-time memory template \(\mathbf{x}_{t,\text{Long}}\). Inspired by the LCT [18], we use a long-term filter \(\mathbf{f}_{\text{Long}}\) to prevent a tracking failure caused by noise interference during long-term tracking. Unlike in the LCT, we do not use the kernel trick [20] for \(\mathbf{f}_{\text{Long}}\) to calculate the response score. Instead, we use a DSST-like approach to calculate the correlation response between the \(\mathbf{x}_{t,\text{Long}}\) and \(\mathbf{f}_{\text{Long}}\) directly. Note that the \(\mathbf{x}_{t,\text{Long}}\) of the \(\mathbf{f}_{\text{Long}}\) can also use projection matrix \(\mathbf{P}_{t}\) for feature compression. The sample obtained from the translation filter is used as the detection sample of the long-term filter. The confidence score of each tracked target \(\mathcal{Z}_{t}=\mathcal{F}\{\mathbf{P}_{t-1}\mathbf{z}_{t}\}\) is computed as \(C_{t,\text{Long}}=\max(\mathbf{f}_{\text{Long}}(\mathcal{Z}_{t}))\). Moreover, the long term memory template \(\mathbf{x}_{t,\text{Long}}\) is updated to the current detection sample when the confidence score is above the predefined threshold.
## 4 Results and Discussion
### Grid Injection from PV Systems into the Grid: Net-Metering Schemes  
Firstly, and from the annual surplus energy estimation examples depicted in Figure 10, a preliminary estimation of annual benefits can be determined by considering the current Spanish legislation--aimed at promoting net-mtering policies--but excluding current taxes on electrical generation summarized in Tables 1-3. With this aim, Figure 11 shows annual estimated economic returns provided by the corresponding PV installations previously determined and summarized in Figure 7. It is important to point out that these benefits are highly dependent on the irrigation profiles required by the crops, and subsequently, they could be different when considering other crops and water needs. Nevertheless, the proposed can be applied to other legislative scenarios.  
From these preliminary analyses, the following results estimate the economic compensation of PV facilities under the current Spanish legislation. Figure 12 gives the benefits under the legislation defined in RD900/2015 and the application of the corresponding taxes and charges. The final economic compensation, compared to Figure 11, is reduced for both 1500 m\({}^{3}\)/ha and 3000 m\({}^{3}\)/ha cases. The analysis of the results and the comparison between economic return on surplus energy for 1500 m\({}^{3}\)/ha and 3000 m\({}^{3}\)/ha, with a law aimed at developing renewable energy and Spanish legislation defined by RD 900/2015, means that only between 40% and 60% of economic compensation for the sale of energy is obtained with application of this legislation regarding a net-mtering scheme excluding taxes and fees. Subsequently, a PV solar configuration for 3000 m\({}^{3}\)/ha allows us to provide between 1.6 and 1.8 times more surplus energy than the 1500 m\({}^{3}\)/ha-based solution. For example, an area of 1000 hectares with an aquifer depth of 30 m and a vineyard crop of 1500 m\({}^{3}\)/ha of annual water requirements is estimated to cost 180 Euro/ha (per year) for the sale of energy. The same solution under current Spanish self-consumption legislation would be significantly reduced by up to 81 Euro/ha (per year).  
Figure 10: PV installation connected to the grid: annual surplus energy estimation examples (1500 m\({}^{3}\)/ha and 3000 m\({}^{3}\)/ha).
## 2 Nonlinear-Trajectory SAR Deception Jamming Algorithm Based on HDE
### Deceptive Jamming Based on HDE
#### 2.3.2 Fast Algorithm of the SAR System-Related Filter Based on HDE  
For nonlinear-trajectory deceptive jamming, it is necessary to avoid calculating trajectory deviation before real-time modulation while ensuring the calculation efficiency of  
Figure 3: Principle of SAR deceptive jamming based on modulation-retransmission. JFR. As far as possible, it is guaranteed that the jammer can construct pulse-by-pulse time-domain trajectory deviations to adapt to the azimuth time-varying trajectory deviations. SAR system-related filters can be represented as:  
\[ \begin{split} H\big{(}x^{\prime},\eta\big{)}=&\exp \left[-j\bigg{(}\eta+\frac{4\pi}{\lambda}\bigg{)}\delta r\big{(}x^{\prime} \big{)}\right]\text{rect}\bigg{(}\frac{\eta}{bcT_{r}}\bigg{)}\iint w^{2}\bigg{(} \frac{x^{\prime}-x_{P}}{L_{a}}\bigg{)}\sigma(x_{P},r_{P})\\ &\times\exp\bigg{\{}-j\bigg{(}\eta+\frac{4\pi}{\lambda}\bigg{)}[r _{P}+\Delta R\big{(}x^{\prime}-x_{P},r_{P}\big{)}+\psi\big{(}x^{\prime},r_{P} \big{)}+\varphi\big{(}x^{\prime},x_{P},r_{P}\big{)}]\bigg{\}}dxdr\\ =&\exp\left[-j\bigg{(}\eta+\frac{4\pi}{\lambda} \bigg{)}\delta r\big{(}x^{\prime}\big{)}\right]\bar{H}\big{(}x^{\prime},\eta \big{)}.\end{split}  \tag{15}\]  
The space-invariant trajectory deviation term \(\delta r(x^{\prime})\), which does not depend on the target's position \((x_{P},r_{P})\), can be separated from double integration. Thus, the space-invariant trajectory deviation construction of the SAR system-related filter of the jammer can be accomplished in the range frequency domain with only one multiplicative calculation. We next discuss the feasibility of efficient calculation of \(\bar{H}(x^{\prime},\eta)\) in the azimuth frequency domain. The Fourier transform expression of \(\bar{H}(x^{\prime},\eta)\) along the azimuth direction is as follows:  
\[ \begin{split}\bar{H}(\xi,\eta)=&\text{rect}\left( \frac{\eta}{bcT_{r}}\right)\int\exp\left[-j\bigg{(}\eta+\frac{4\pi}{\lambda} \bigg{)}r_{P}\right]dr\iint\sigma(x_{P},r_{P})w^{2}\bigg{(}\frac{x^{\prime}-x _{P}}{L_{a}}\bigg{)}\\ &\times\exp\left[-j\bigg{(}\eta+\frac{4\pi}{\lambda}\bigg{)} \big{[}\Delta R\big{(}x^{\prime}-x_{P},r_{P}\big{)}+\psi\big{(}x^{\prime},r_{ P}\big{)}+\varphi\big{(}x^{\prime},x_{P},r_{P}\big{)}\big{]}\right]\exp\big{[}-j \xi x^{\prime}\big{]}dxdx^{\prime},\end{split}  \tag{16}\]  
where \(\xi\) is the azimuth frequency, \(\xi=2\pi f_{a}/v\), \(f_{a}\) is the azimuth frequency.
# Interdependent Transcription of a Natural Sense/Antisense Transcripts Pair (_SLC34A1_/Pfn3)
## 2 Results
### Protein Expression  
The mRNA for _SLC34A1/PFN3_ sense and antisense was found in both nucleus and cytoplasm with moderate enrichment for the transporter-encoding transcript in the cytoplasm (Figure 3C). Hence, we investigated whether the increased levels of _SLC34A1_ mRNA gave rise to functional Na/Pi transport protein.  
Two different experimental approaches were pursued, including epithelial cell uptake and functional expression, in _Xenopus laevis_ oocytes. The experiments revealed low expression of the protein/function that was close to detection level. HEK293 cell monolayers were treated with dexamethasone for 5 days, and tested for the uptake of radioactive phosphate. There was phosphate uptake through unspecified carriers producing significant background, nevertheless, stimulation of _SLC34A1_ expression through dexamethasone approximately doubled the transport of phosphate (Figure 7A). To obtain further confirmation that the dexamethasone stimulated output from the _SLC34A1/PFN3_ locus was biologically active, we purified total RNA from wild type and knock-in cells (WT, WT + dex, antisense knock-in, antisense knock-in + dex). The mRNA was injected into _Xenopus_ oocytes, followed by uptake measurements after 3-4 days. Again, the effects were small but a stimulatory effect of both knock-in and dexamethasone treatment was observed (Figure 7B).  
To summarize, we have shown that the _SLC34A1/PFN3_ sense-antisense transcripts interfere at the level of transcription in a cell-specific manner. We propose a model whereby dexamethasone stimulates the expression of the transporter-encoding sense gene. Altered chromatin marks parallel the transcription on the sense strand which, in turn, promotes low-level transcription of the _PFN3_-antisense gene. The concordant interaction is active in cis but not in trans. The increased transcript levels appear to have a minor impact on protein expression.  
Figure 6: Transient expression of sense/antisense cassettes in HEK293 cells. Cells were transfected with two doses (50 and 250 ng/mL) of cDNA encoding, either the sense transcript (dark grey) or the antisense transcript (light grey). Expression levels of sense and antisense transcripts were assayed by RT-qPCR to detect potential post-transcriptional stabilizing effects. All values are related to untransfected HEK293 cells. To estimate PCR background, GFP was amplified in all samples (control and shaded area reflecting non-significant changes).
# Construction of Color Prediction Model for Damaged Korla Pears during Storage Period
## 2 Materials and Methods  
### Test Materials  
Korla fragrant pear samples were collected from 14-year-old pear trees with generally consistent canopy sizes in Shiertuan Fragrant Garden, Alear City, on 15 September 2021, which is the general harvest period in Xinjiang. Fragrant pears with similar shapes, uniform size, uniform color, no distortion, no insect diseases, and no mechanical damages were chosen as the test samples. The collected samples were weighted at 110 \(\pm\) 5 g.
## 3 Materials and Methods
### Insitu Reservoir Pressure, Stress, and Permeability Data  
The insitu reservoir pressure, stress, and permeability data of CBM wells in the Dacun Block were collected, and these data were obtained through injection/fall-off well testing conducted via a coal geological engineering survey and design institute in Sichuan. The injection/fall-off well testing is a single-well pressure transient test, in which a certain amount of water is injected into the reservoir at a constant rate for a period of time and then the well is shut in for pressure recovery. The bottom hole pressure data during injection and shut-in are recorded, respectively, and the reservoir parameters, such as insitu reservoir pressure, stress, and permeability data, are calculated through this test.
# Low Complexity Equalization of Orthogonal Chirp Division Multiplexing in Doubly-Selective Channels
## 3 Low Complexity Equalizers for UP-OCDM under Doubly Selective Channels  
In this section, based on the transmission model, we propose the MMSE equalization first. Then based on the special circulant structure of \(\mathbf{\Phi}\), two low complexity equalization algorithms for UP-OCDM are proposed. The first low complexity algorithm is based on LDL\({}^{H}\) factorization of the banded approximation of the equivalent channel matrix, and the second one is based on the LSQR algorithm, which is known as an iterative Krylov subspace method. Note that since the transform matrix in OCDM is also circulant, the proposed equalizers can be directly applied for OCDM systems over doubly selective channels.
## 2 Basic DC/DC Converters  
Typically, the classification of DC to DC converters is generally divided in two types, according to whether they increase (step-up) or decrease the voltage (step-down) (as disscused in [19]).  
The step-up converter is usually known as boost converter (Figure 1), meanwhile the step-down converter is commonly known as buck converter (Figure 2). In both boost and buck topologies, the key elements for the power conversion are the input voltage \(V_{\text{g}}\), the \(L\) inductance, the output capacitance \(C\), and the load of the system (resistance \(R\)).  
The way the components interact between them leads to the power conversion, which directly depends on the configuration of the components and on their relation with the gates in the circuit.  
Figure 1: Boost converter model.  
Figure 2: Buck converter model.  
In the case of Figures 2 and 1, the Diode \(D\) and the N-Channel MOSFET \(Q\) are the components that allow switching states between the charge and discharge of the inductance.  
Hence, the inductor is the main element responsible of controlling energy flow to the output capacitor. Together, the output elements achieve a filter behavior that finally results in a \"constant\" output power to the load.  
As explained in [6], the topologies of the buck and boost converters are the basis for the construction of many other topologies, since interconnecting series or parallel buck and/or boost converters give rise to other converters, such as the inverting and the non-inverting buck-boost converters.  
Nevertheless, despite of the different configurations of the different converters, all the circuits have some common elements, such as the inductance \(L\) linked to the switch-node, and the inductance charging dependence on the control switches. Therefore, the correct selection of inductors remains a problem, whose solution leads to improve the efficiency of the performance of the converter.  
Therefore, the main modeling approaches for DC/DC converters are the Average and the Small-Signal techniques. Nevertheless, both have different limitations, in the case of average state space model, according to [20] the approach is not able to simulate ripple effect on the inductance current and output voltage, due to the nonexistent switching frequency parameter; additionally, it does not allow an accurate closed-loop controller simulation, owing to the absence of a parameter involving the duty cycle of the switching device accurately.  
Regardless, the Small-Signal analysis model used by [21] and also explained in [6], still has some of the limitations of the average model. Nevertheless, as explained in [22], the model approximates the dynamic behavior of the system around an initial steady-state, but again without considering the inductance current and output voltage ripple. Nevertheless, the introduction of the duty cycle term in the model allows to correctly study the behavior of the system around an operating point, and also the behavior of the circuit against disturbances.
###### Abstract  
Source enumeration is an important procedure for radio direction-of-arrival finding in the multiple signal classification (MUSIC) algorithm. The most widely used source enumeration approaches are based on the eigenvalues themselves of the covariance matrix obtained from the received signal. However, they have shortcomings such as the imperfect accuracy even at a high signal-to-noise ratio (SNR), the poor performance at low SNR, and the limited detection number of sources. This paper proposestwo source enumeration approaches using the ratio of eigenvalue gaps and the threshold trained by a machine learning based clustering algorithm for gaps of normalized eigenvalues, respectively. In the first approach, a criterion formula derived with eigenvalue gaps is used to determine the number of sources, where the formula has maximum value. In the second approach, datasets of normalized eigenvalue gaps are generated for the machine learning based clustering algorithm and the optimal threshold for estimation of the number of sources are derived, which minimizes source enumeration error probability. Simulation results show that our proposed approaches are superior to the conventional approaches from both the estimation accuracy and numerical detectability extent points of view. The results demonstrate that the second proposed approach has the feasibility to improve source enumeration performance if appropriate learning datasets are sufficiently provided.  
electronic warfare; source enumeration; eigenvalues of covariance matrix; subspace-based estimation; uniform linear array; machine learning; Gaussian mixture model +
Footnote †: journal: Applied Sciences
# MAC Layer Protocols for Internet of Things: A Survey
## 3 Long Range MAC Layer Protocols
### NB-IoT
in addition to the HARQ (Hybrid Automatic Repeat Request) confirmation information for the uplink data channel [93]. The NPDSCH channel carries higher layer data as well as paging message, system information and random access response message. In uplink, the NB-IoT includes two physical channels that are described as (i) Narrowband Physical Random Access Channel (NPRACH) and (ii) Narrowband Physical Uplink Shared Channel (NPUSCH). NPRACH is a newly designed random access channel substituting the legacy LACH random access channel and uses a bandwidth of 1.08 MHz. It is more than the uplink bandwidth for the NB-IoT. The NPUSCH channel has two formats. The first is used to load uplink data and has a maximum block size of 1000 bits [94], which is much smaller than the LTE legacy. The second format is used to signal Hybrid Automatic Repeat reQuest (HARQ) recognition for the NPDSCH, and uses a repeat code for error correction. NB-IoT reduces extreme simplicity for delay-tolerant applications such as meters, devices and sensors, providing wider coverage [95]. Common IoT solutions require low cost, high network device density and low data transfer. The NB-IoT technology aims, through LTE optimization, to meet this demand (more than 50 thousand devices) with low data rates in delay-tolerant applications. Figure 19 consolidate key parameters of NB-IoT.
# Correction of Severe Class III Malocclusion by mandibular
###### Abstract  
This retrospective investigation aims to evaluate the dentoskeletal and soft tissue changes after the distalization of the mandibular condition using the ramal plates in nonextraction camouflage treatment of severe Class III malocclusion with a full-cusp discrepancy or more at the first molars. The sample consisted of pre- (T1) and post-treatment (T2) lateral cephalograms from 25 adult patients who were treated with the ramal plates for mandibular total distalization. The skeletal, dental, and soft tissue variables were analyzed from T1 to T2 in order to determine the effects of mandibular dentional distalization. The mandibular first molars distalized 3.65 mm at the crown and 1.81 mm at the root. Similarly, the mandibular incisors retracted 3.32 mm at the crown and 0.81 mm at the root. Skeletally, the Wits appraisal displayed a significant increase of 1.56 mm. Also, soft tissue changes showed a significant lower lip retraction of 1.26 mm. These findings suggest that the ramal plates exhibited effective mandibular distalization in treating severe Class III malocclusion, which may be considered a viable alternative to the orthognathistic surgical approach in some patients.  
Keywords:ramal plate; severe class III malocclusion; mandibular distalization; nonextraction treatment +
Footnote †: journal: applied sciences