esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Effect of BSA Step on Interfacial Adhesion Between the ENM and Cu Layer
(**e**) TEM elemental mapping images of C, N, O, S, and Pd. (**f**) SEM and EDS mapping images of the peeled tape adhesive surface for (**Left**) ENMH and (**Right**) ENMH(\(-\)BSA step) showing the distribution of residual Cu. (**g**) Surface coverage of Cu particles on the tape as a function of the number of peel cycles. (**h**) Sheet resistance of crosslinked samples after repeated peel tests. Error bars represent standard deviation (\(n\geq 5\)). Changes in sheet resistance following repeated peel cycles were also evaluated (Figure 3h). For the ENMH, sheet resistance increased gradually from 2.542 \(\pm\) 0.135 \(\Omega\)/sq (Bare) to 3.712 \(\pm\) 0.125, 4.025 \(\pm\) 1.491, 7.859 \(\pm\) 1.379, 8.248 \(\pm\) 2.004, and 9.982 \(\pm\) 3.521 \(\Omega\)/sq after one to five peel cycles, respectively. This represents a 2.7-fold increase between the first and fifth cycles, while maintaining relatively stable electrical conductivity under repeated mechanical stress. In contrast, the ENMH(-BSA step) exhibited a sharp increase in sheet resistance, from 124.300 \(\pm\) 71.240 \(\Omega\)/sq (Bare) to 175.890 \(\pm\) 89.632, 345.880 \(\pm\) 463.550, 871.235 \(\pm\) 598.637, 2963.520 \(\pm\) 1752.960, and 4781.520 \(\pm\) 2352.100 \(\Omega\)/sq over the same five cycles (Figure S4). This corresponds to a 27.2-fold increase from the first to the fifth cycle. Notably, the sheet resistance of the ENMH(-BSA step) after the fifth cycle was approximately 479 times higher than that of the ENMH, highlighting the critical role of the BSA layer in enhancing interfacial durability and electrical stability.
## 2 Principle and Overall Design of Frequency Stabilization System
### Basic Principle
curve is wider, and the signal amplitude is larger, which helps to improve the long-term locking ability of the frequency stabilization system [25].
## 3 Data and Methods
### Methods
#### 3.2.3 Correction Methods
The blue discrete points are the data along the profile line, the black dashed line is the mean value of these data, and the red solid line is the fitted straight line considering the asymptotic tendency of the attitude jitter stripes. R2 and R3 denote the satellite numbers. where \(P(1)\), \(P(2)\) are unknown parameters, \(Y_{i}\) is the fitting matrix used to eliminate the attitude jitter distortions, \(\Delta y\) ensures the coherence of attitude jitter along the parallel flight direction, and sign is the positive and negative sign matrices in the same dimension as the offset matrix. The error correction effect is shown in Figure 4.  
Inter-satellite bias is a systematic error introduced by different imaging conditions among satellites of the RapidEye constellation during earth observation. When the error magnitude is small, it can be removed by the improved \"mean elimination method\". When the error magnitude is large enough to completely mask the surface deformation information, it can be rejected directly.
# Deep Learning Architectures for Diagnosis of Diabetic Retinopathy
## 2 Materials and Methods
### Models
#### 2.5.3 ConvMixer  
Finally, we trained the ConvMixer model [20]. It is an extremely simple model that is similar in spirit to the ViT (input patches representation and isotropic architecture repeating the same block structure) but relies exclusively on convolutions to extract image features and to combine the information across the multiple layers. More specifically, it makes use of depth-wise and point-wise convolutions for mixing the extracted features across spatial and channel dimensions, respectively. Furthermore, it maintains equal size and resolution throughout the network. When this model was presented, it outperformed different ViTs and ResNets on ImageNet 1k [20]. This model was originally developed to perform classification problems so we had to adapt the last part of the model in order to be able to address a segmentation problem. To achieve this, we added a final transpose convolution layer (symmetrical to the initial one that divides the image into patches) which goes from h channels to a single channel, using a kernel size and stride equal to the patches size. The result of this layer is a single-channel mask of the same size as the input image. An additional skip connection is added to this mask which sums (instead of concatenates) the result of this layer with a channel mean of the input image. This is done to improve the convergence during the training. Figure 3 shows the ConvMixer with the additional segmentation output modification.  
Figure 3: Original ConvMixer architecture plus the final modification to face segmentation problems. Adapted with permission from Ref. [20]. 2021, [PERSON].  
We have trained two versions of this model, one called ConvMixer and another with less trainable parameters, which we called ConvMixer-Light. In the ConvMixer architecture, we selected a patch size of 3 pixels, an embedding dimension of 375, a depth of 20 layers and kernels of size \(3\times 3\). With this configuration, the number of trainable parameters increases up to 2.97M.  
For the training of this model, we iterated over a total of 1500 epochs, using an initial learning rate of 0.002, Adam optimizer, and a batch size of 2 images. We found the best hyperparameters for the lost function at \(\theta=7\) and \(\alpha=0.8\).  
For the ConvMixer-light version, we decreased the depth from 20 to 14 layers and the embedding dimension from 375 to 128. With this modification, we reduced the number of trainable parameters to a total of just 270k.  
We have trained the ConvMixer-light model for 1000 epochs, using an initial learning rate of 0.002, Adam optimizer, and a batch size of 2 images. We found that the best hyperparameters for the lost function are \(\theta=1.5\) and \(\alpha=0.7\).
Clinical Application and Biomechanics of Various Lingual Retractors with Temporary Skeletal Anchorage Devices: A Narrative Review  
[PERSON]  
1 Department of Orthodontics, Kyung Hee University Dental Hospital, Seoul 02447, Korea 11 Department of Orthodontics, Kyung Hee University Dental Hospital, Seoul 02447, Korea 11 Department of Orthodontics, Kyung Hee University Dental Hospital, Seoul 02447, Korea 11 Department of Orthodontics, School of Dentistry, Kyung Hee University, Seoul 02447, Korea 11 Department of Orthodontics, Graduate School of Dentistry, Kyung Hee University, Seoul 02447, Korea 11 Department of Orthodon  
to the bracket, and the point of application of the orthodontic force is far from the CR of the anterior teeth toward the occlusal surface, rendering the control of tooth movement difficult. A lingual retractor can alter the direction and position of the retraction force to the anterior CR by adjusting the length of the lever arm [4; 6; 7; 8; 9].  
Early lingual retractors used the transpalatal arch (TPA) with soldered hooks connected to the first and second molars for intra-arch anchorage [10; 11; 12]; however, since 2005, temporary skeletal anchorage devices (TsADs) such as mini-implants or palatal plates, have gained wide usage for intra-arch anchorage. TsADs allow more precise torque and vertical control [2; 4; 6; 13; 14]. TsADs placed in the palatal bone have a high success rate owing to the excellent bone quality of the palate [15; 16; 17].  
The design of the lingual retractor has continuously evolved over the past 20 years. Consequently, the C-lingual retractor (CLR) [2; 3; 9; 10; 11; 12; 13], lingual lever arm4, double J retractor (DJR) [7; 14], and anterior-posterior lingual retractor (APLR) [5; 13], have been devised and presented. Although many articles on lingual retractors have been published, most studies have discussed the clinical applications and treatment results of only one type of lingual retractor. This narrative review aimed to first discuss and present the clinical applications and biomechanics of various lingual retractors with TsADs introduced to date.
# Characterization of Two Hydrogen Peroxide Resistant Peroxidases from _Rhodococcus apacus_ 1 Cp
## 2 Materials and Methods
### Cloning and Expression  
The _dyp_ genes (_dypA_--RHA1_ro05773 and _dypB_--RHA1 ro02407) from _Rhodococcus jostii_ RHA1 (CP000431.1) were compared to the sequences (_dypA_--R1 CP_10130 and _dypB_--R1 CP_29905) from _R. opacus_ 1 CP (CP009111.1). The genes _dypA_ and _dypB_ were amplified from genomic DNA of _R. opacus_ 1 CP using the primer pairs for _dypA_ (DyPA_1 CP_fw and DyPB_1 CP_rev) and _dypB_(DyPB_1 CP_fw and DyPB_1 CP_rev) (Appendix A). The PCR products were obtained from agarose gel electrophoresis, blunt-end ligated into the dephosphorylated HincII (for \(dypA\)) and Sfol (for \(dypB\)) restriction site of pUC19 and then propagated into _E. coli_ DH10B. Plasmids were isolated from white colonies and subjected to DNA sequencing (data not shown).  
Confirmed \(dypA\) and \(dypB\) genes in pUC19 were cleaved at the _Ndel_ and _Notl_ restriction site and ligated into the pET16 bP vector. Correct clones were selected by colony PCR and expression plasmids were sent for DNA sequencing using T7- and T7 term- primers. Plasmids with the confirmed \(dyp\) genes constructs were then transformed to _E. coli_ Rosetta (DE3) pLysS.  
For gene expression, precultures of the expression clones were made by inoculating the _E. coli_ harboring \(dypA\) and \(dypB\) in an LB medium and then subsequently placed the cultures at 37 \({}^{\circ}\)C at 150 rpm overnight. Precultures were then transferred into a 500 mL fresh LB medium containing the antibiotics, ampicillin and chloramphenicol, with the aforementioned concentration and 16 \(\upmu\)M hemin. Expression cultures were cultivated at 37 \({}^{\circ}\)C at 150 rpm. Once the cultures reach an OD\({}_{600}\) of about 0.5, the temperature was decreased to 20 \({}^{\circ}\)C and subsequently induced with 0.1 mM IPTG (isopropyl-\(\beta\)-D-1-thiogalactopyranoside). Cells were harvested at 4 \({}^{\circ}\)C by centrifugation (5000 rpm, 20 min) and cell pellets were frozen at \(-\)20 \({}^{\circ}\)C.
# Analyzing Multiscale Spatial Relationships between the House Price and Visual Environment Factors
## 2 Framework
### Model Construction  
#### 2.4.1 Hedonic Price Model  
The global regression is conducted by the hedonic price model (HPM). The HPM has three common forms: linear models, semilog models, and double-log models. In this study, the double-log form was adopted to build HPM because of the distribution of model variables. Based on the basic theory of the HPM, the double-log form HPM can explain the percentage change in the dependent variable caused by each unit of change in one dependent variable when the other independent variables are held constant. The HPM formula is given below.  
\[\ln(P_{i})=\beta_{0}+\sum_{j=1}^{k}\beta_{j}x_{ij}+\varepsilon \tag{7}\]  
In Formula (7), \(P_{i}\) is the housing transaction price at the \(i\)th neighborhood unit, \(\ln(P_{i})\) is the natural logarithm of \(P_{i}\), \(\beta_{0}\) is a constant, \(x_{ij}\) is the \(j\)th independent variables for the \(i\)th neighborhood unit, \(\beta_{j}\) is the corresponding estimated coefficient, and \(\varepsilon\) is an error term.
## 3 Results
### Albert Rift Regional Tectonostratigraphy  
The integrated analysis comprising seismic and well data, together with insights from the literature, reveals the tectonostratigraphic framework of the Albert Rift, highlighting the basin's stratigraphy, depositional environments, and tectonic phases. The results indicate a complex tectonostratigraphic evolution, driven by repeated extensional tectonics and subsidence, which controlled sedimentation patterns and the development of hydrocarbon plays across different sectors of the rift. The Albert Rift is divided, from north to south, into distinct sectors: Rhino Camp, Pakwach/Murchison Falls, Kaiso-Tonya/Central Lake Albert, Southern Lake Albert/Semliki Valley, and the Basins Area around Lakes Edward-George Basin. Each sector exhibits unique tectonic and depositional characteristics, contributing to the basin's petroleum potential. In this section we present the lithostratigraphy, and subsequently the tectonostratigraphy, of four of the five sectors, based on data availability.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Lithologic Unit** & **Age Description** & **Characteristics** & **Tectonic and Depositional Interpretation** \\ \hline Nyamusika Formation & Mid-Piocene & Dominated by fine-grained, organic-rich sediments, including shales and fine sands interspersed with gastropod shells. & Rebresents the rift expansion stage with widespread tectonic subsidence.  
The stratigraphic framework is derived from the integration of lithological characteristics from outcrops, seismic attributes, wireline log signatures, mud-log descriptions, faunal and palynological assemblages, and tephra data used for dating and correlation.
## 5 Results & Discussion  
### Simulation Results of the Proposed Method  
As shown in the Figure 13, reversible power data are collected for the synchronous generator at 200 MVA/11 KVA, 60 Hz, Load10 MW and 3 MVAR. The reverse energy relay function is to prevent a reverse energy state in which the energy flow from the Basbar is towards the generator. In Figure 4, the proposed security migration function appears. The inverted power data collected during the transition from the abnormal state of the generator to the engine position will be monitored using current, voltage, active and reactive data. In this sense, this will be monitoring the changes in the time domain of mechanical power, rotor speed and frequency, which is shown in the Figure 14b.  
Figure 14: (a) Performance of input–output power; and (b) relay status.  
Figure 13: Model of a Reverse Power Relay for Smart Villages.  
We used protective relays to collect reverse power data. The relay can also detect interference. When these disturbances occur, all signals are processed through the fuzzy neural unit, including the fault and after the fault interval. We believe that the connection to the wireless sensor network can obtain reverse energy data measurements.  
The structure of our neuro-fuzzy controller is depicted in Figure 4. The nodes input_1 and input_2 represent the input variables and deliver their crisp values to the modules containing the respective membership functions. The relay is tested under various scenarios. We have also provided details of this system in Appendix A Table A2.  
**Case 1:** Under normal conditions, the mechanical input of the generators varies in the range of 0.6-0.7 pu at 1-2 s. The input/output power and the observed status are shown in Figure 14. In this case, the relay does not trip.  
**Case 2:** In this case, the input of the mechanical generator changes from 0.7 to \(-\)0.1 pu at 2-3 s. The status
## 3 Results
### Land Use and Land Cover Distribution
#### 3.2.3 Buffer Areas Around Conservation Units  
The analysis of buffer zones surrounding conservation units revealed patterns largely consistent with the overall land cover distribution in both study areas (Tables A7-A12). Forest classes remained the predominant land cover type within these buffer zones, reflecting the broader landscape composition.  
In Manaus, the buffer zones around Local full protection conservation units (Table A9) exhibited a distinct land cover distribution. Development Areas emerged as the dominant land cover type, occupying 46.5% (5314 ha) of the total buffer area. Porto Velho demonstrated an even more pronounced anthropogenic influence in the buffer zones of its Local-Level conservation units (Table A12). The areas surrounding both management types of Local-Level conservation units were primarily characterized by human-modified landscapes. Agriculture/Pastureland dominated the buffer zones of full protection units, covering 33.1% (3985 ha) of the area, while Development Areas were prevalent in buffer zones of sustainable use units, accounting for an overwhelming 82.4% (2539 ha) of the total area.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Changes** & **Total Area Manaus (ha)** & **Total Area Porto Velho (ha)** \\ \hline
0 change & 927,019 & 622,061 \\
1 change & 60,268 & 87,886 \\
2 changes & 89,438 & 119,617 \\
3 changes & 41,245 & 59,940 \\
4 changes & 20,105 & 27,782 \\
5 changes & 4129 & 6138 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Frequency of yearly LULC changes in the study areas by hectares (ha).  
Porto Velho exhibited more dramatic fluctuations in both deforestation and degradation (Figure 2). Deforestation peaked in 2020-2021, with 14,653 ha of forest converted, primarily to Agriculture/Pastureland (9869 ha, accounting for 67.4% of the total deforestation). Following this peak, deforestation declined by approximately half in 2021-2022 and remained relatively stable in subsequent years. Forest degradation in Porto Velho was most pronounced in 2018-2019, affecting 31,157 ha. After reaching its lowest level the following year, degradation showed an increasing trend in subsequent years.  
In contrast, Manaus displayed a more gradual increase in deforested areas (Figure 2). Starting from 2018-2019, deforestation steadily rose, culminating in 5267 ha of forest loss in 2022-2023. This final year's figure narrowly surpassed the previous year's record by just 3 ha. Similar to Porto Velho, the primary driver of deforestation in Manaus was the conversion of forest areas to Agriculture/Pastureland, accounting for 4625 ha (87.8% of the total deforestation area) in the final year. Forest degradation in Manaus peaked earlier in the study period, with 26,500 ha affected in 2018-2019, followed by fluctuating rates in subsequent years.
## 2 Signal Model
A rough estimation of carrier frequency and code phase of the received signal is obtained from the acquisition process, which initialize the related parameters of the tracking channel. Then the estimation of these two signal parameters is refined step by step through the tracking loop. The numerically controlled oscillator (NCO) in the tracking loop produces mutually orthogonal sine and cosine signals, which can be created as:  
\[\left\{ \begin{array}{ll}\mu_{os}(t)=\sin(2\pi f_{NCO}t+\varphi_{NCO})\\ \mu_{oc}(t)=\cos(2\pi f_{NCO}t+\varphi_{NCO})\end{array} \right. \tag{6}\]  
where \(f_{NCO}\) and \(\varphi_{NCO}\) is the carrier \(NCO\) frequency and initial phase respectively. In this research, the TCXO is used in the TC-OFDM receiver, and the accuracy of the TCXO is 0.5 ppm. After many real tests, we find that the phase noise of the receiver clock is smaller than white Gaussian noise in the research of the TC-OFDM meter-level positioning accuracy. Also, we can use the TCXO phase noise estimation algorithm to predict the actual phase noise. However, adding the TCXOphase estimation algorithm has less influence on the proposed algorithm in this research. Therefore, the effect of phase noise can be ignored in this research. After carrier stripped, the coherent integration result of I and Q channel signals by integration and dumping module can be created as:  
\[\left\{ \begin{array}{l}Corr(n)=A_{mps}(n)\sin c(\Delta fT_{coh})e^{(j(2\pi \Delta f/nT_{coh}+\Delta\varphi)}+\omega(n)\\ A_{mp}=A_{IF}(n)T_{coh},\ \Delta f=f_{d}-f_{NCO},\ \Delta\varphi=\varphi_{IF}- \varphi_{NCO}\end{array} \right. \tag{7}\]  
where \(Corr(n)\) represents the \(n\)th coherent integration result, \(T_{coh}\) is the coherent integration duration, \(s(n)\) denotes the positioning-assisted information, \(A_{mp}\), \(\Delta f\) and \(\Delta\varphi\) represent the signal amplitude, carrier and phase residual respectively. In order to avoid bit transition, the coherent integration duration used generally do not exceed one time slot duration. We assume that in the coherent integration duration, \(s(n)\) can be regarded as a fixed value. For the convenience of analysis, we assume that the local code is strictly aligned with the received signal. When in the coherent integration duration, the parameters \(A_{mp}\), \(\Delta f\) and \(\Delta\varphi\) have minor changes which can be regarded as unknown nonrandom parameters. In the tracking process, the term of \(\text{sinc}(\Delta fT_{coh})\approx 1\) when \(\Delta f\) is small enough. Then Equation (8) can be simplified as:  
\[Corr(n)\approx A_{mps}(n)e^{(j(2\pi\Delta f/nT_{coh}+\Delta\varphi)}+\omega(n) \tag{8}\]  
The conventional carrier loop structure is shown in Figure 2. The coherent integration results of Equation (8) is sent into the carrier discriminator to estimate carrier frequency. Then the Carrier NCO is adjusted in real time based on the carrier frequency filtering results from the loop filter.
# THBase: A Coprocessor-Based Scheme for Big Trajectory Data Management
## 2 Preliminary  
### Related Definition  
There are usually two forms of trajectory data: discrete and continuous. In this paper, we use discrete form to represent trajectory data. The data types of trajectory are defined as follows:  
**Definition 1**.: _A trajectory point \(p\) is a location-based data element, denoted as <moid, \(l\), \(t\), o>, where moid is the MOID of an MO, \(l\) and \(t\) are the spatial and timestamp information, and o is the other information which includes speed, heading, etc._  
**Definition 2**.: _A trajectory \(T\) contains a sequence of ship trajectory points (\(p_{1}\),..., \(p_{m}\)), orderly by their timestamps, and all points of \(T\) belong to the same MO._  
**Definition 3**.: _A trajectory segment (TS) contains the trajectory points of an MO sampled in a TI (Time Interval). Where TI is generated by unified division in time dimension, namely all the TIs are of the same length, so the start time of a TI can be its identifier. From the definition, it is easy to know that a trajectory segment can be uniquely identified by MOID and TI attributes._  
In Euclidean Space with \(n\) dimension, given a coordinate point \(q\) = (\(q_{1}\), 2026, \(q_{n}\)) and a rectangle \(E\) = (\(s\),\(r\)), where \(s\) = (\(s_{1}\),..., \(s_{n}\)) and \(r\) = (\(r_{1}\),..., \(r_{n}\)) are the two vertices on the main diagonal of \(E\), and for all \(k\) = 1 to \(n\), \(r_{k}\geq s_{k}\). For the distance between \(E\) and \(q\), there are the following definitions:  
**Definition 4**.: _Mindist(E,q) is the shortest distance from \(E\) to \(q\)[17]; it is computed as below:_  
\[\mathrm{Mindist}(E,q)=(\sum_{i=1}^{n}|E_{i}-q_{i}|^{2})^{1/2}. \tag{1}\]  
_where:_  
\[E_{i}=\left\{\begin{array}{ll}s_{i}&\mathrm{if}\ q_{i}<s_{i}\\ r_{i}&\mathrm{if}\ q_{i}>r_{i}\\ q_{i}&\mathrm{otherwise}\end{array}\right.. \tag{2}\]  
**Definition 5**.: _MinMaxdist(E, q) is the minimum overall dimensions distance from \(q\) to the furthest point of the closest face of E [17]; it can be expressed as:_  
\[\mathrm{MinMaxdist}(E,q)=(\min(\,|E_{mk}-q_{k}|^{2}+\sum_{\begin{array}{c}i \
eq k\\ 1\leq i\leq n\end{array}}|E_{mi}-q_{i}|^{2}))^{1/2}. \tag{3}\]  
_where:_  
\[\begin{array}{ll}1\leq k\leq n\\ E_{mk}=\left\{\begin{array}{ll}s_{k}&\mathrm{if}\ q_{k}\leq(s_{k}+t_{k})/2\\ t_{k}&\mathrm{otherwise}\end{array}\right.\\ E_{mi}=\left\{\begin{array}{ll}s_{i}&\mathrm{if}\ q_{i}\geq(s_{i}+t_{i})/2\\ t_{i}&\mathrm{otherwise}\end{array}\right.\end{array}\right.\end{array} \tag{4}\]