esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Beam Profiles  
Angular beam profile measurements for both orientations of the PIN diode emitter were performed with the Golay cell for a frequency range of 100 GHz to 1000 GHz at 50 GHz intervals. The resulting profiles are shown in Figure 4a,b. All profiles were normalised to allow for ease of comparison, since the power ratio between the low and high frequencies is over two orders of magnitude (see Figure 3).  
The H-plane profiles in Figure 4a, while not perfectly Gaussian, show a single peak centred around 0\({}^{\circ}\) for frequencies below 400 GHz. Between 400 GHz and 600 GHz, the single peak becomes significantly broader and shows significant small features throughout the profile. Above 600 GHz, the centre of the peak skews to the negative angles.  
In Figure 4b, the E-plane profiles for frequencies below 250 GHz also show a peak centred at 0\({}^{\circ}\); however, unlike the H-plane profiles, there are also significant side lobes either side of the centre. Above 300 GHz, these side lobes disappear, but the main peak becomes heavily skewed to the positive angles and highly asymmetrical.  
While the bow-tie antenna itself is symmetric, the feeding lines and the InP substrate are not. When the wavelength is of a similar order as the dimensions of the feeding point structures or the substrate, those structures start to radiate or cause refraction. The influence of the feeding point geometry on the beam profile is studied in a separate publication.  
Figure 3: **(a)** Power output of the PIN diode as a function of frequency. Power was measured with a calibrated pyroelectric detector for frequencies below 500 GHz and with a Golay cell for 200 GHz to 1000 GHz. **(b)** Optical setup for power measurement.
###### Abstract  
Algal filtrate and homogenate, obtained from Baltic seaweeds, were applied in seedling growth tests. Radish seeds were used in order to assess algal products phytotoxicity and their biostimulant effect on growth and nutrient uptake. Algal filtrate, at concentrations ranging from 5.0% to 100% was used for seed soaking and as a liquid biostimulant (soil and foliar application). Algal homogenate was developed for seed coating. Algal filtrate and homogenate were also enriched with Zn(II) ions in order to examine the influence on metal ion complexation. The optimal doses of algal filtrate and homogenate, as well as soaking time were established. Multi-elemental analyses of the raw biomass, filtrate, homogenate, and radish were also performed using ICP-OES (Inductively Coupled Plasma--Optical Emission Spectrometry). The best results in terms of seedlings' length and weight were obtained using clear filtrate at a concentration of 50% applied to the soil and for homogenate applied at a dose of 50 mg/g of seeds. Clear filtrate at a concentration of 50% used for seed soaking for one hour showed the best results. The applied algal products increased the content of elements in seedlings. Among the tested products, a concentration of 50% algal filtrate is recommended for future pot and field experiments.  
Baltic macroalgae; filtrate/homogenate; chelating properties; biostimulant/seed coating; soaking; growth enhancement/seedling growth test; analytical methods
# Assessment and Spatial Modelling of Agrochernozem Properties for Reclamation Measurements
## 2 Materials and Methods
### Environmental Variables  
The environmental covariates used for digital mapping included Sentinel-2A satellite data (S2A) and topographic attributes. S2A data were selected during the period with minimal vegetation (bare soil). The dataset of 25 May 2019, which was used for study, matched these parameters. Satellite bands and the Normalized Difference Vegetation Index (NDVI) were used. Relief attributes were calculated using a 30 m resolution digital relief model (NASA's Shuttle Radar Topography Mission (SRTM), [[https://www2.jpl.nasa.gov/srtm](https://www2.jpl.nasa.gov/srtm)]([https://www2.jpl.nasa.gov/srtm](https://www2.jpl.nasa.gov/srtm)) (accessed on 20 January 2023)). The topographical attributes included elevation, aspect, slope, multiresolution of ridge top flatness (MrRTF) and (MrVBF) multiresolution valley bottom flatness indices. The environmental variables used for modelling are presented in Table 1.
## 3 The Proposed Tracker
### Updating Strategy  
During tracking, the appearance of the object will change due to many factors, such as deformation and rotation. Furthermore, the target object may be occluded by other objects. A conventional CF tracker, such as KCF, used a fixed learning rate to updating the model. If the tracker is occluded or drifts, the correlation filter will be contaminated. To tackle this problem, we propose an adaptive updating scheme by using a fluctuation parameter.  
In the process of deforming or occluding for the object, we want to gradually reduce the learning rate to avoid updating much confusion into model. When the target is restoring, the learning rate should gradually increase to adapt the change of model. The deviation of PSR between two consecutive frames can describe the degree which appearance changing is alleviating or deteriorating. Based on the above motivation, we propose a fluctuation parameter \(m\), the fluctuation parameter can adaptively adjust the updating rate for different frames. Moreover, when PSR score drops to a threshold, it is an indication that the object is heavy-occluded or tracking has failed. Therefore, the correlation filter shouldn't be updated when PSR score is less than the threshold.  
For both holistic and local components, the fluctuation parameter of \(t\)-\(th\) frame can be computed as below:  
\[m_{i}^{t}=exp\Big{(}g\big{(}\rho_{i}^{t}\big{)}-g\Big{(}\rho_{i}^{t-1}\Big{)} \Big{)},\ t>1 \tag{12}\]  
\[g(z)=\frac{1}{1+e^{-(\rho_{t}-THR)}} \tag{13}\]  
where the \(THR\) denotes the PSR threshold.  
Finally, if \(\rho_{i}^{t}>THR\), this component will be updated individually by its own learning rate \(\gamma_{i}^{t}=m_{i}^{t}\beta\), else its model will be prohibited updating. Where the \(\beta\) is an initial learning rate.
###### Abstract  
Digital isolators are implemented to protect low-voltage electronics and ensure human safety during high-voltage surge events. In this work, we present the design of an inductive-based digital isolation system that can sustain up to 1 KV\({}_{\mathrm{rms}}\) breakdown voltage. The proposed system is designed using the pulse polarity modulation scheme and fabricated in a 0.35 \(\upmu\)m CMOS. Two identical dies are bounded within the IC package, with one die housing the transmitter (Tx) and the isolation transformer, while the other die contains the receiver (Rx). Two different customized designs between three metal layers are implemented to form the isolation element. The transformer's secondary coil is constructed in metal-1, while the primary coil is formed in metal-2 and metal-3 for comparing the system functionality, isolation capability, and propagation delay. The functionality has been verified by measurements for an operating frequency of 300 MHz with a 2.6 ns propagation delay and an energy consumption of 8.15 \(\times\) 10\({}^{3}\) pJ/bit at 1 Mbps. The chip was tested under extreme temperatures and achieved a maximum measured common mode transient immunity (CMTI) of 500 V/us. Jitter has been examined to ensure fast transmission at a bit error rate (BER) of 10\({}^{-15}\) with a total jitter (TJ) of 188.18 ps.  
breakable; digital isolator; inductive isolation; integrated transformer; industrial applications; system-on-chip applications; gate driver; pulse polarity modulation +
Footnote †: journal: Electronics Letters
## 3 Proposed Method
### Attention-Guided Transformer Module (AGTM)  
As the pivotal component of the proposed method, AGTM comprises two blocks: the Attention Guiding Block (AGB) and the Channel-wise Multi-head Transformer Block (CMTB). To ensure the feature extraction and enhancement quality on multi-scale features, the AGB has been bifurcated into two separate types: the Attention Guiding Block--Top (AGB-T) in the encoding/decoding stage and the Attention Guiding Block--Bottleneck (AGB-B) in the bottleneck stage. AGTM at the top of the encoder-decoder network (AGTM-T) promotes both local facial details and global facial structures, while AGTM at the bottleneck side (AGTM-B) optimizes the encoded low-level features. Furthermore, noticing that the usual spatial-wise transformers are limited to position-specific windows and their partition strategy may potentially alter the structure of facial images, the Channel-wise Multi-head Transformer Block (CMTB) is introduced here to achieve an image-size receptive field by utilizing feature map channels. The AGB and CMTB are complementary and can facilitate the simultaneous promotion of both local facial details and global facial structures.
## 1 Introduction  
In 1899, [PERSON] firstly described the importance of the position of the upper first permanent molar (UFM) in the diagnosis and planning of the orthodontic treatment. Thus, the UFM is considered the \"key of occlusion\" because of its stable anatomical position in relationship with the skull base, and its normal position [1; 2; 3; 4] with the mesiobuccal cusp occluding in the buccal groove of the mandibular first molar.  
Among the possible alterations of the UFM position, the occurrence of a mesiopalatal rotation in the previous literature seems to reach a prevalence of 83% in a population of subjects with presence of malocclusion [5; 6; 7], or even higher in subjects with class II malocclusion where it ranges from 83% to 95% [5; 8; 9; 10]. Some authors hypothesized that the rotation itself could influence the correct occlusion with the lower first molar, generating a class II malocclusion [8; 9]. UFM rotation has a considerable clinical relevance because it is able to influence three major aspects of the orthodontic treatment, as the molar relation in the sagittal plane [6; 11], the available space in arch perimeter [12; 13; 14; 15; 16; 17], and the long-term stability of orthodontic treatment outcomes related with better interidental contact points [18; 19; 20].  
As there are few studies in literature evaluating the prevalence of UFM mesiopalatal rotation [1; 5; 6; 8; 9; 11; 12; 21], its relationship with dental occlusion was not as fully analyzed as, for example, the relationship with dental crowding and molar class relationship. Furthermore, most of the studies analyzed the prevalence of the UFM mesiopalatal rotation only in permanent dentition.  
The aim of the present study is to analyze the association of mesiopalatal UFM rotation with anterior dental crowding in a sample of subjects with mixed or permanent dentition.
## 4 Interference Characterization
### System Performance  
#### 4.4.1 Spatial Average Rate  
_Spatial average rate_ is a measure of the quantity of users or services that can be simultaneously supported by a limited radio frequency bandwidth in a defined geographic area. It is defined as \(\bar{R}=E\left[\ln\left(1+SINR\right)\right]\), where the expectation is with respect to the random fading channels and the random positions of the transmitter nodes.  
Assume that the PDF of SINR is \(f_{SINR}\left(x\right)\). The spatial average rate can be expressed as  
\[\int\limits_{x=0}^{\infty}\ln\left(1+x\right)f_{SINR}\left(x \right)dx\] \[=\int\limits_{z=0}^{\infty}\frac{1}{1+z}dz\int\limits_{x=z}^{ \infty}f_{SINR}\left(x\right)dx\] \[=\int\limits_{z=0}^{\infty}\frac{1-P_{o}\left(z\right)}{1+z}dz \tag{7}\]  
Equation (7) is achieved because  
\[F_{SINR}\left(z\right)=\int\limits_{x=0}^{z}f_{SINR}\left(x\right)dx=P_{o}\]  
according to the definition of outage probability.
## 5 Conclusions  
The combination of measurements, sensors, platforms, and processing techniques is fundamental for better understanding the processes ongoing along coastal areas such as land subsidence, sea-level rise, and volcanic activities. The spatial and temporal changes of such phenomena must be measured accurately, and the contribution from several driving factors must be estimated and separated. In such a context, we analyzed by InSAR data some case studies along the Italian coastlines affected by either small or extreme deformation rates, and strictly correlated with natural and human-based risks, such as volcanic and hydrogeological risks.  
The experimental results suggest that InSAR data, together with geomorphological assessments and in situ measurements, contribute to unveil the complexity of geohazards along the Italian coastlines.  
Then, in view of developing an appropriate infrastructure for the storage and semi-automatic processing of S1 SAR data, we believe that A-DInSAR methods have the capacity to perform a near real-time surveillance service if applied to SAR data with a short revisit time, i.e., six days.  
Some steps toward this have already been done [35; 36], but performing a robust and reliable service should be the aim of the entire SAR scientific community in order to support the responsible organizations in designing optimal risk mitigation strategies in coastal areas characterized by significant hazard levels.  
Conceptualization, M.P., M.A. and A.M.; Data curation, S.F. and C.L.P.; Investigation, M.P., A.M., C.B. and C.T.; Supervision, F.D., S.Sa. and S.St.; Visualization, M.P.; Writing--original draft, M.P., M.A., A.M., C.B., C.T. and G.P.; Writing--review & editing, F.D., S.Sa. and S.St.  
This research received no external funding.  
The authors thank the European Space Agency and the Italian Space Agency for providing ENVISAT and Cosmo-SkyMed SAR data. A special mention is due to projects and research activities carried out by INGV at both national and international levels (in particular, MISE-DGMRE, PRIN 2011-2013, MARISK within ERANETMED network and SAVEMEDCOASTS), whose results have represented a valuable suppport to the work and the analyses presented in the manuscript  
The authors declare no conflict of interest.
## 1 Introduction
[PERSON] [27] combined both CNN and GRU to fully utilize the temporal and spatial information of the bearing vibration signals and effectively improve the prediction accuracy of the bearing's RUL, but the method is only applicable to a single load. [PERSON] [28] achieved the RUL prediction of bearings by feature mining through CNN, normalizing the obtained monotonicity and temporal degradation features as the HIs of bearings, and inputting them into an LSTM network. [PERSON] [29] successfully combined CNN and GRU to achieve a high-precision prediction of bearings, while also verifying that the performance of the CNN-GRU network is superior to a single network. Although all of the above methods have achieved certain results in the field of bearing RUL prediction, they still have certain shortcomings. First, the existing deep learning-based bearing RUL prediction methods usually select the variables that react to the bearing degradation trend from the time or frequency domain of the bearing signal and perform dimensionality reduction through principal component analyses and other methods to obtain the HI of the bearing, which is a cumbersome procedure, overly relies on the experience of the experts, and does not necessarily react well to the degradation of the bearings. Secondly, the existing deep learning prediction methods mainly focus on the RULprediction of single-loaded bearings, and it is difficult to predict the RUL of bearings under variable-load conditions; moreover, the existing deep learning bearing RUL prediction methods need to use a large amount of data to train the model, which makes the prediction difficult when there are a lack of training data. The above problems bring challenges to accurately predict the RUL of rolling bearings under variable load conditions. In this paper, we propose a new HI construction method, which combines CNN-GRU-MHA and migration learning to construct a prediction model, realizing a high-accuracy prediction of the RUL of rolling bearings under variable load conditions with a small amount of data, and verifying the performance of the proposed model based on the PHM2012 and XJTU-SY datasets.
## 2 Theoretical Background  
### Total Productive Maintenance  
Total Productive Maintenance, or TPM, is a maintenance strategy first created in 1971 in Japan at Nippondenso, a Toyota supplier, and later standardized by the Japan Institute of Plant Maintenance (JIPM) [9, 13]. TPM has been influenced by the American preventive maintenance idea and follows lean manufacturing philosophy with a basic principle: achieving sustainable industrial performance requires collective responsibility for equipment management [9].  
Unlike conventional methods that are based on corrective maintenance carried out by specialists, TPM focuses on the proactive and preventive engagement of all employees [9]. Its key focus is to maximize Overall Equipment Effectiveness (OEE) by reducing breakdowns, unplanned downtime, quality defects, and performance losses [14, 15].  
TPM is based on eight basic pillars [16], including Autonomous Maintenance, where operators are directly responsible for performing fundamental equipment maintenance, creating a sense of responsibility [17]; Preventive Maintenance through scheduled examinations to avoid failure [18]; and Focused Improvement, where efforts are aimed at the cause of losses, specifically the sixteen major categories [18]. The Education and Training pillar also focuses on developing a skilled and multidisciplinary workforce making active contributions towards TPM [17].  
These pillars carry TPM over to office work, development management, and the critical areas of environment, health, and safety, underpinning the commitment to a safe and sustainable workplace [17, 18]. Altogether, these pillars promote a sense of discipline and responsibility, and a spirit of continuous improvement, which encourage organizations to strive towards the ideal of \"zero breakdowns, zero defects, zero accidents\" [19].
# Study and Analysis of Interference Signals of the LTE System of the GNSS Receiver
## 4 Simulation Studies
### Reception of Interference Signals
In this situation, each segment of the solid-phase plane has a specific center of curvature that is at different points for different azimuth and elevation angles. Therefore, the position of the antenna phase center is a function of the angles \(\Theta\) and \(\Psi\). Professional antennas have a specially designed structure to minimize this variability. Figure 5: Arrangement of antennas receiving navigation signals. The second factor interfering with measurements in applications requiring the highest precision is the variation of the antenna phase centre position depending on the incident wavelength. This is important when multi-frequency receivers are used, but they are not the subject of this research. In practical applications, this is not a problem as the elements are attached to a conductive surface that reflects the signals coming from the half-space on the side of the plane where there are no elements. The plane of the array should be parallel to the surface of the Earth, with elements placed on top, to enable the reception of GNSS signals from all directions for which the satellite-receiver interface is directly visible. In receivers, especially the most advanced ones, numerous techniques of detecting and eliminating interfering signals are used (e.g., taking into account only the signal that was received the earliest); however, the key factor improving signal quality is appropriate characteristics of GNSS antenna limiting the phenomenon of a multipath signal, which were obtained thanks to its specific design and the use of appropriate materials. In the literature, this phenomenon is referred to as the \"mitigation of multipath effect\", which is in particular used in the CDMA system (Code Division Multiple Access). However, such antennas are very expensive and have relatively large dimensions, so they are basically used only for applications requiring ultra-precise measurements (e.g., GNSS reference stations).
## 7 Risk Analysis
### Tcu
#### 7.2.5 Repudiation  
1. **Component** _Security risk_: In the event of an ongoing MITM attack targeting the TCU communication, the absence of non-repudiation techniques becomes a critical concern. This vulnerability opens the door for the attacker to either steal sensitive data or inject falsified information into the system. In the latter scenario, the system would incorporate these data alongside legitimate inputs, eroding its reliability and undermining its intended functionality. Consequently, both the optimal operation of the vehicle and the safety of the driver could be compromised, especially in the event of a serious incident necessitating the activation of the ACN. _Security measure_: Among the most potent strategies to establish non-repudiation is the implementation of digital signatures utilizing asymmetric keys issued by trusted third parties. This approach instills confidence in the message recipient regarding the origin of the content, ensuring its authenticity. Also, in this case, TPM can be used to reduce the overhead introduced with digital signatures.
# An Integrity Monitoring Method for Navigation Satellites Based on Multi-Source Observation Links
## 3 Design of Integrity Monitoring System with Multi-Source Observation Links  
### Demand Analysis  
LEO satellites can be used as satellite-based monitoring stations for navigation satellites to make up for the limitations mentioned above. The advantages of LEO satellites for BDS-3 monitoring can be reflected in the following aspects.  
1. Extending monitoring area  
At present, approximately 60% of daily messages from the MEO satellite cannot be returned to the main control station due to the limited layout of ground monitoring stations. LEO satellites have characteristics of a high kinematic velocity and information-carrying rate, making it possible to realize multi-tracking of the full-arc satellite and extend monitoring content. Based on the communication ability of LEO satellites, messages can be returned at the minute level, or even the second level, no matter where the navigation satellite is.
2. Improving availability of integrity parameters  
Calculation of integrity parameters, which can indicate the accuracy of service, usually needs observation data collected by ground monitoring stations. With the support of LEO satellites, more observation data can be gained to participate in the calculation of integrity parameters, making it possible to globally realize real-time broadcasting of integrity parameters. Moreover, the accuracy of signal-in-space could be obviously improved when the orbits of navigation satellites and LEO satellites are synchronously determined [16]. The accuracy of corresponding integrity parameters will also be improved, thus improving the indication accuracy of the navigation service.  
Therefore, constructing multi-source observation links with the support of ground monitoring stations and LEO satellites has great significance in breaking through thebottleneck of existing technology and effectively improves reliability and continuity of integrity monitoring.