esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Sensing for Irrigation of Horticultural Crops
## 3 Sensors and Platforms  
[PERSON] et al. [31] reported that the first aerial images were taken by a camera mounted on a balloon around 1859. From the very beginning of satellite-based remote sensing in the 1970s, the use of remote sensing for terrestrial applications has increased tremendously [32]. Remote sensing is the acquisition of information about an object, or event through the analysis of data acquired obtained by a device without contact with the target under investigation [33]. The term remote sensing includes _all the traditional primary mapping data acquisition technologies_[34].  
Remote-sensing instruments are designed to identify various wavelengths of the electromagnetic spectrum referred to as a \"_band\"_ or \"_channel\"_. Each instrument is characterized by a specific number and widths of wavelengths detected; some instruments detect discrete bands, while others detect fairly narrow wavelengths or broader bands (multispectral, visible and near-infrared wavelengths, microwave, thermal sensing systems) [35; 36; 37].  
Remote sensing is able to identify spectral signatures of all surfaces and objects. Sensors can sense targets with a specific spatial resolution of image data defined by the smallest spatial area sampled or viewed. Digital images are made up of pixels, each pixel characterized by specific spectral and spatial attributes [38], as the intensity of reflectance or emittance measured by a sensor [39]. Spatial resolution corresponds to the spatial area each displayed or printed pixel represents.  
When the time image was shot (day or year) is a crucial point in interpreting remotely sensed data analysis [40]. If the same area is imaged in the morning and late afternoon, a greater number of features with different orientations may be discerned than if the area is only imaged in the morning [41]; this is especially true when interpreting vegetation classes. Multi-temporal images are taken at different times to monitor changes in the environment and to analyze numerous processes [42; 43]. The temporal dynamics may range from hours to a number of years. [PERSON] et al. [44] and [PERSON] et al. [45] reported that \"to assess changes over a time sequence accurately, effects in the data not caused by true environmental changes (such as differing atmospheric conditions and sun or view angle positions) must first be accounted for\".  
Other sensors are defined as bidirectional, due to the illumination angle at which the energy source strikes the target, and the position (angle) at which the instrument receives the emitted or the reflected energy (called the view angle) as described by [PERSON] [46] and [PERSON] [47].
## 2 Problem Description and System Models
### Safety Constraints for Reactive Power Optimization  
The secure operation constraints include voltage constraints for all bus nodes and current constraints for network branches. Under safe operating conditions, the bus voltage magnitudes and branch currents must satisfy the following conditions:  
\[\begin{cases}0.95\leqslant U_{i}\leqslant 1.05\ \ \ i\in\mathcal{N}\\ \ \ I_{ii}\leqslant I_{ii}^{\max}\ \ \ i\in\Omega\end{cases} \tag{4}\]  
where \(U_{i}\) denotes the bus voltage magnitude at node \(i\), and \(\mathcal{N}\) represents the set of nodes. \(I_{ii}\) is the current flowing through branch \(il\), \(I_{ii}^{\max}\) denotes the maximum allowable current for branch, and \(\Omega\) represents the set of branches.
# 60 Years of Studies into the Initiation of Chromosome Replication in Bacteria
## 3 The DnaA Protein
### Detailed Analysis of oriC Opening by DnaA In Vitro  
#### 3.3.1 Loop-Back Model  
[PERSON]'s group tried to analyse the mechanism of _oriC_ opening by DnaA-ATP, step-by-step, using _M13_ plasmid DNA (7.7 kb) containing _oriC_[50; 51]. The opening of _oriC_ was detected by digestion of the substrate with P1 nuclease specific for a single-stranded  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Region \({}^{\ddagger}\)** & **Properties** \\ \hline \hline \(\mathrm{II}\) & Dia interaction [43], Dps interaction [34] \\ \(\mathrm{II}\)78 & Can be deleted or replaced by YFP [44], deletion suppresses _seqA objE_[35] \\ \(\mathrm{III}\)136 & Walker ATP-binding motif; ssDNA-binding sites V211 and R245 [37], AAA+ Arg finger motif important for _oriC_ interaction [45], lysine178 \\  & can be acetylated by YfiQ, leading to inactivation [46] \\ \(\mathrm{IIIb}\)295 & Loss of membrane-binding due to deletion [38], cAMP-binding site [39], ATPase mutant [40], cold-sensitive mutants: R334H, R42H, E361H [41] \\ \(\mathrm{IV}\)374 & Sequence-specific DNA-binding [30; 47] \\ \hline \hline \end{tabular} \({}^{\dagger}\) Number of the first amino acid according to [32].  
\end{table}
Table 1: Domain traits of the DnaA protein.  
DNA. _oriC_ (246 bp) contains four DnaA boxes (R1 and R4 with high affinity, and R2 and R5M with medium affinity), as well as several low-affinity DnaA-binding sites (R2, I1, I2, C3, C2, I3, and C1). The lower affinity-binding boxes allow continuous binding of DnaA over the whole region. Other important elements participating in the opening include the DUE, which contains three AT-rich, 13-mers, and an IHF-binding site.  
According to the results of [PERSON]'s group, the first step in initiation is the binding of DnaA-ATP to R1 and R5M, and then, after DnaA-ATP binding to low-affinity sites, IHF bends _oriC_ by 180\({}^{\circ}\) in such a way that the DUE faces the DnaA-ATP oligomers. This hypothetical scheme, later named the \"loop-back model\" (Figure 1), was re-examined by considering the peptide structures of both Domain III and IV [51; 52]. DnaA-ATP bound to single-stranded DNA around R1 or R5M opens the DUE and forms a complex with the T-rich single strand of the DUE attached to V211 and R245 of the Domain IIIa of DnaA. Then, the DnaB helicase is loaded at the opened site in order to continue unwinding (by forming a pre-replication complex). It should be noted that IHF is not essential for initiation in vitro, since its absence only disturbs normal initiation in vivo (see below).
# A Low Sampling Rate Receiver Design for Multi-Antenna Multi-User OFDM Systems
## 3 Proposed Low Sampling Rate Design
### Equivalent Channel Model  
Substituting (28) into (27), we obtain:  
\[\mathbf{\tilde{y}}=c\mathbf{d}+\mathbf{\tilde{z}}. \tag{30}\]  
For the \(k\)-th user, the \(v\)-th sample is:  
\[\hat{y}_{k}(v)=c\mathbf{d}_{k}(v)+\mathbf{z}_{k}(v), \tag{31}\]  
for \(v=0,1,\cdots,V-1\) and \(k=1,2,\cdots,K\). This means that the outputs of the ADC are directly the desired data symbols, with scaling and polluted by AWGN. This is the reason that we have omitted the FFT module from Figure 2.  
Similar to (14) and (16), the average BER and average MI of the proposed system are obtained by:  
\[\text{BER}^{\text{Prop}}=\mathbb{E}\left[\frac{1}{2}\text{erfc}\bigg{(}\sqrt{c ^{2}\frac{E_{\text{b}}}{N_{0}}}\bigg{)}\right] \tag{32}\]  
and  
\[\text{MI}^{\text{Prop}}=2-\frac{1}{\ln 2}-\mathbb{E}\left[\log_{2}\left(\sum_{j =1}^{4}\text{e}^{-\left\|\mathbf{z}_{k}(v)+c(\mathbf{z}_{1}-v_{j})\right\|^{2}}\right) \right], \tag{33}\]  
respectively.
## 1 Introduction
This increase has created a need to non-invasively monitor and control relevant variables during the production, in particular those relating to seafood safety and to the welfare and health of the fish. Some studies have been conducted to build up machine-based systems for fish disease diagnosis. In those studies the main challenge is to develop the knowledge database, a task which is time and resource consuming and focuses mainly on water quality [21] and/or on nutritional problems, parasites, viruses, bacteria and fungal agent diseases [22]. Other studies aim at developing early warning systems [23], including the use of sms alert procedures [24]. The main challenge for these fish-health oriented studies is the development of the decision component rather than the warning systems itself. In addition, suboptimal environmental conditions, such as hypoxia, feeding regime [25], high fish density [26, 27], the presence contaminants in the water including human drugs [28] and hypochlorite, and the water's pH [16] have been shown to alter fish behaviour. This opens the possibility of using fish behaviour itself as a biomarker for environmental monitoring and in aquaculture settings. Fish behaviour is very complex [29] and the response to a stochastic event may be considered one aspect of it. Following [PERSON]'s idea [1], we were interested in studying the coherent response of the group rather than the individual response of each fish, which in addition to requiring much more computational effort it may be impractical in real-life settings where there may be several thousand individuals in the same area or cage. The objective of the present work was to develop a methodology for image acquisition, processing and nonlinear trajectory analysis of the collective fish response to a stochastic event. The acquisition of the information of the movement of the fish can be achieved by images, typically video recording [30, 31, 32] and/or by echo-sounds [25]. The information thus obtained may then be processed by different nonlinear algorithms as indicated above. Video recording was chosen for its simplicity and FD and Entropy for their proven suitability to identify nonlinear features.
## 2 LFC, INP, and CBRR  
### Lfc  
An interconnected power system consists of a large number of CAs that are connected via tie-lines. Each individual TSO maintains the frequency of each CA within predefined standard limits and the tie-line power flows with neighboring CAs within prespecified tolerances, which are the main objectives of the LFC [28]. This is generally accomplished by reducing the ACE, which is, for the CA \(i\), defined as \[ACE_{i}=\Delta P_{i}+B_{i}\Delta f_{i}, \tag{1}\]  
where \(\Delta P_{i}=(P_{ai}-P_{si})\) and \(\Delta f_{i}=(f_{ai}-f_{si})\) denote interchange power variation and frequency deviation, respectively. Note that \(P_{ai}\) and \(f_{ai}\) denote actual values, while \(P_{si}\) and \(f_{si}\) denote scheduled values. Furthermore, \(B_{i}\) is the frequency-bias coefficient [1]. Note that \(ACE_{i}>0\) denotes that the generation is higher than the load; hence, the CA is denoted as \"long\". Similarly, a CA is denoted as \"short\" when \(ACE_{i}<0\).  
The basic LFC framework of the CA \(i\) is shown in Figure 2, where LPF denotes a low pass filter and SH a sample and hold, with the value of a sampling time \(T_{\rm s}=2\) s. A negative control-feedback is characterized as \(-1\) gain and PI denotes a proportional-integral controller. Scheduled control power \(\Delta P_{sci}\) denotes the output of LFC, which is appointed to the participating control units that change the electrical control power \(\Delta P_{ei}\) appropriately. Neglecting the losses, then \(\Delta P_{ei}\) is, for the CA \(i\), defined as  
\[\Delta P_{ei}=\Delta P_{Li}+\Delta P_{i}, \tag{2}\]  
where \(\Delta P_{Li}\) denotes the load-power variation. Note that \(\Delta P_{ei}\) is well-known as the balancing energy, whereas, instead of LFC reserve, the term RR is generally used.
# Boundary-Aware Transformer for Optic Cup and Disc Segmentation in Fundus Images
###### Abstract  
Segmentation of the Optic Disc (OD) and Optic Cup (OC) boundaries in fundus images is a critical step for early glaucoma diagnosis, but accurate segmentation is challenging due to low boundary contrast and significant anatomical variability. To address these challenges, this study proposes a novel segmentation framework that integrates structure-preserving data augmentation, Boundary-aware Transformer Attention (BAT), and Geometry-aware Loss. We enhance data diversity while preserving vascular and tissue structures through truncated Gaussian-based sampling and colormap transformations. BAT strengthens boundary recognition by globally learning the inclusion relationship between the OD and OC within the skip connection paths of U-Net. Additionally, Geometry-aware Loss, which combines the normalized Hausdorff Distance with the Dice Loss, reduces fine-grained boundary errors and improves boundary precision. The proposed model outperforms existing state-of-the-art models across five public datasets--DRIONS-DB, Drishti-GS, REFUGE, G1020, and ORIGA--and achieves Dice scores of 0.9127 on Drishti-GS and 0.9014 on REFUGE for OC segmentation. For joint segmentation of the OD and OC, it attains high Dice scores of 0.9892 on REFUGE, 0.9782 on G1020, and 0.9879 on ORIGA. Ablation studies validate the independent contributions of each component and demonstrate their synergistic effect when combined. Furthermore, the proposed model more accurately captures the relative size and spatial alignment of the OD and OC and produces smooth and consistent boundary predictions in clinically significant regions such as the region of interest (ROI). These results support the clinical applicability of the proposed method in medical image analysis tasks requiring precise, boundary-focused segmentation.  
fundus image; optic disc; optic cup; boundary-aware transformer attention; geometry-aware loss; structure-preserving data augmentation; segmentation; glaucoma; medical image analysis +
Footnote †: journal: Medical Image Analysis  
1
###### Abstract  
The aim of the present investigation is to evaluate the implant therapy outcomes over a period of 5 years and to analyze several patient risk factors influencing the stability of the peri-implant tissues. Seventy-eight patients were consecutively treated between 2009 and 2017 and restored with implant-supported fixed prostheses. The following inclusion criteria were considered: partial or complete edentulism; residual bone volume of at least 3.3 mm in diameter and 8 mm in length; a favorable relationship between maxilla and mandible; at least a minimum 5 year follow-up for each implant included in the statistical analysis. Intraoral radiographs were taken at implant loading and every 12 months during the follow-up visits. They were subsequently stored on a personal computer and analyzed to determine the changes in bone level. Seventy-eight patients receiving 209 implants completed a minimum follow-up period of 5 years. One-hundred dental implants were inserted in the maxilla while 109 were placed in the mandible. Eleven (14.1%) out of 78 treated patients who received 29 (13.9%) dental implants were considered as drop-outs. On the whole, peri-implantitis was diagnosed in three implants. The average final pocket probing depth at implant level was 2.5 \(\pm\) 1.2 mm. The average final bone loss after 5 years was 0.3 \(\pm\) 0.4 mm, both at the mesial and distal aspect of the implant. The effects of the prosthesis type, sex and implant site did not statistically influence the marginal bone loss; on the contrary, a statistically significant difference regarding marginal bone loss was detected between smoker and non-smoker patients (\(p\) = 0.021). Implants with internal-conical abutment connection showed stable peri-implant bone levels at the medium-term follow-up. Nevertheless, further prospective long-term clinical studies are necessary to confirm these data.  
internal-conical-connection; partial fixed dentures; marginal bone level +
Footnote †: journal: Medical Imaging
## 2 Materials and Methods
### Patient Data  
This retrospective study was performed with ethical approval from the Institutional Review Board of Kokura Memorial Hospital. We chose 113 follow-up MRA images prior to rupture, including 103 images of 18 UA cases and 10 images of RA cases, from 28 patients (35-92 years, median: 63 years) who underwent MRA for intracranial aneurysms examination. Patient characteristics are shown in Table 1. We selected mainly MCA aneurysms for the following two reasons based on a large Japanese cohort study (6697 aneurysms) [2]: (1) since the MCA is the most frequent location (36.2%) of intracranial aneurysms, it would not be difficult to collect them; (2) because MCA aneurysms relatively tend not to rupture, we can easily aggregate their follow-up images. The MRI images were acquired on a 3.0T scanner (SIGNA EXCITE HDxt 3.0T, GE Healthcare, Chicago, IL, USA). Parameters for the three-dimensional (3D) time-of-flight (TOF) MRA were set as follows: spoiled gradient-echo sequence with TR/TE/FA, 23 msec/3.4 msec/20\({}^{\circ}\); FOV/Matrix, 200 \(\times\) 229 mm/224 \(\times\) 224; slice thickness/slice overlap, 1.2 mm/50%; ASSET factor, 2; actual bandwidth, 31 kHz. Anisotropic MRA images and ROI were transformed into isotropic images with an isovoxel size of 0.5 \(\times\) 0.5 \(\times\) 0.5 mm\({}^{3}\), using cubic and shape-based interpolations [20], respectively. A neurosurgeon delineated the contours of the aneurysms as ROI for this study on the MRA images using the 3D slicer of open-source software [21]. The ROI of the aneurysms were employed to calculate radiomic image features on the MRA images. The aneurysm diameters ranged from 2 mm to 12 mm (median, 4.43 mm).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Characteristics** & **Total of Patient Cohort** \\ \hline No. of patients & 28 \\ No. of ruptured cases & 10 \\ No. of unruptured cases & 18 \\ Follow-up MRA images & 113 \\ No. of images for ruptured cases & 10 \\ No. of images for unruptured cases & 103 \\ \hline
**Gender** & \\ \hline Male & 9 \\ Female & 19 \\ Age (year) & 35–92 (median: 63) \\ Aneurysm diameter (mm) & 2–12 (median: 4.43) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Patient characteristics.  
Figure 1: An overall workflow of a proposed model for prediction of degree of intracranial aneurysm rupture.
###### Abstract
Based on the conducted research, the GEP algorithm delivered more accurate results than others. Hence, the corresponding mathematical relationship will be used for \(V_{s}\) prediction in the oilfield. Compared to the available empirical methods which use only one parameter to estimate the shear wave velocity, the novel correlation extracted via the GEP technique considers four parameters to predict the \(V_{s}\) values. This advantage results in more accurate predictions of \(V_{s}\) values in the oilfield. It is noteworthy that the obtained mathematical relationship can be also used in other reservoirs containing identical geological conditions. The structure of this article has been arranged as follows: Firstly, in Section 2.1, a brief description of the oilfield project is elaborated. Then, in Section 2.2, the raw well logging data are presented. Next, in Section 2.3.1, the linear correlations between the different well logging parameters and \(V_{s}\) are extracted using the LR technique. Afterwards, in Section 2.3.2, the basic formulations related to the five applied empirical correlations are explained. Then, the basics of the MLR and GEP methods are described in Sections 2.3.3 and 2.3.4, respectively. Thereafter, in Section 3, the findings derived from the conducted research are presented. Following that, Section 4 is dedicated to discussing the obtained results. Finally, in the Conclusions section, the article ends with a concise description about the key findings, results, future works, and implications.
# Indoor Localization Using Semi-Supervised Manifold Alignment with Dimension Expansion
## 4 Testing Results
### Testing Results
From this figure, we can find that as the ratio between the number of calibrated and labeled locations increases, the localization accuracy also increases. For instance, the mean error under the ratio of 30% (or by using 28 calibrated locations) is 3.3 m, while the one under the ratio of 100% (or by using 93 calibrated locations) decreases to 2.1 m. Figure 3e shows the variation the CDFs of errors as the number of RSS measurements collected at each calibrated location increases from 150 to 200. As expected, a larger number of collected RSS measurements results in a higher localization accuracy. Finally, based on the same layout in Figure 2, we give a simple example about the localization performance by using the single RSS measurement, double RSS and AOA measurements and triple RSS, AOA and TOA measurements for localization, respectively, in Figure 3f. In the testing, the standard deviations of AOA and TOA measurements are assumed to be evenly distributed in the ranges of [0.5\({}^{\circ}\), 5\({}^{\circ}\)] and [0.3 ns, 6 ns], respectively, which are also used in [39]. From this figure, we can find that more types of measurements used for localization generally result in a higher localization accuracy, which triggers the motivation of generalizing the proposed approach to the scenario with multiple types of measurements used for localization. Figure 2: Layout of an indoor environment. Figure 3: Parameter discussion in simulation. (**a**) Sum of elements in \(\mathbf{P}_{x}\) and \(\mathbf{P}_{y}\) under different values \(\Delta\); (**b**) CDFs (Cumulative Distribution Functions) of errors under different noise perturbations; (**c**) CDFs of errors under different signal attenuation properties; (**d**) CDFs of errors under different numbers of calibrated locations; (**e**) CDFs of errors under different numbers of RSS measurements; (**f**) CDFs of errors by using different types of measurements for localization.
## 3 Results and Discussion  
### Resistivity, Magnetoresistivity, and Hall Coefficient  
Measurements of resistivity, magnetoresistivity and Hall coefficient were carried out in the magnetic field of 0.65 T.  
As shown in Figure 2, at the temperature of 290 K the resistivity increases at increasing of Sb concentration and has higher value for thinner film. The character of the magnetoresistivity dependence on Sb content is opposite. A small value of magnetoresistivity shows that the main factor limiting charge carriers is their concentration. The studied film thicknesses are away from quantum oscillations range of thicknesses. Hence, in stationary conditions, we can assume the carrier concentration is the same for all the films from 70 to 150 nm for one composition. Figure 2c shows that for most of the films the Hall coefficient is negative and depends on external factors insignificantly. Negative sign of Hall coefficient shows that electron mobility is higher than hole one. It seems that dependency of Hall coefficient on Sb content is rather complex, and such behavior is due to variation of Bi\({}_{1-x}\)Sb\({}_{x}\) band structure. The first calculation of thin-film bismuth-antimony band structure was performed in work of [PERSON] et al. for different film thicknesses using dual-zone model [11].  
Mobility and concentration of electrons and holes in the trigonal plane (the film plane) were calculated using Equations (7)-(9). Calculation takes into account that solid solutions of bismuth-antimony are intrinsic semimetals which means that they have equal concentrations of electrons and holes. Mobility ratios for electrons (\(u_{1}^{-},\mu_{2}^{-}\)) were taken from bulk crystal analysis. It allows a reduction of unknown values to three of them, which makes the Gauss method applicable for solving the equation system in stationary case.  
Figure 2 shows the mobility and concentration of electrons and holes. The graph shows that the dependence of carrier concentration has a minimum at 5 at. %. The mobility of charge carriers weakly depends on the concentration of Sb, since the scattering of carriers occurs on themselves. For 150 nm film on mica, the electron and hole mobilities are both higher in the case of 3 % Sb. Thus, the value of Hall coefficient at this Sb content demonstrates that the hole conductivity makes the main contribution.