esa-sceva/satcom-chunk-collection huggingface dataset:
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 6 HARRF SPDT Design
### Key Performance Metrics
#### 6.4.3 Return Loss  
Return loss measures how much of the signal reflects back to the source, typically due to impedance mismatches. It is defined by:  
\[RL(f)=-20\log_{10}\bigl{|}\Gamma\bigr{|},\quad\Gamma=\frac{Z_{\text{in}}-Z_{0 }}{Z_{\text{in}}+Z_{0}}, \tag{56}\]  
where \(Z_{\text{in}}\) is the input impedance at the reference plane and \(Z_{0}\) is the characteristic impedance (commonly \(50\,\Omega\)).  
A distributed topology is employed for the SPDT switch to achieve broadband performance, as depicted in Figure 18. Specifically, the design combines:  
* Two series FETs to improve power handling and ensure operation down to DC.
* Four shunt-stacked FET units to enhance isolation by absorbing the off-state capacitance into a low-pass filter section.  
One challenge in designing such a reconfigurable network with an SPDT switch is to maintain optimal impedance matching in both switch states (\"ON\" and \"OFF\") to minimize insertion loss. Large resistors (>ku) are used at the source and drain terminals (labelled as V1 and V2 in Figure 18) for biasing, thereby minimising additional loss in the RF path. The drain voltages for the series FETs alternate between 0 V and 2 V, depending on whether a particular arm is in the \"ON\" or \"OFF\" state (see Table 3).  
When the left arm is active (ON), the series FETs on that arm are biased at \(V_{g}=0\) V (low \(R_{\text{on}}\)), while the shunt FETs in that arm are pinched off (high \(R_{\text{off}}\)). Meanwhile, the right arm remains in the OFF state, with its series FETs biased at \(V_{g}=-1.5\) V, thereby achieving high isolation. The gate control voltages V1 and V2 act in a complementary fashion: when V1 is low, V2 is high, and vice versa. This arrangement ensures that at any time only one path is active (ON) while the other path is isolated (OFF).
## 6 Conclusions  
This study proposes a WWSF algorithm to resolve color, contrast, and texture deficiencies in optical images collected during underwater inspections of hydraulic structures. Extensive qualitative and quantitative experiments on dam-inspection imagery confirm its effectiveness. The principal findings are the following:  
(1) WWSF consistently achieves the highest or near-highest scores across three reference-free evaluation metrics--UIQM, AG, and EN--outperforming existing state-of-the-art methods. This confirms its effectiveness in enhancing underwater imagery for real-world engineering applications.  
(2) By applying Sobel edge energy weighting to high-frequency wavelet sub-bands, WWSF enhances fine-scale defect features such as cracks, spalling, and corrosion more effectively than traditional average-gradient-based approaches. Simultaneously, it suppresses background noise and irrelevant textures.  
(3) The algorithm maintains strong performance in terms of contrast and color fidelity even in highly turbid waters. However, partial loss of micro-texture is still observed under severe scattering conditions, indicating a limitation in extreme environments.  
(4) This study indicates that future research should focus on integrating turbidity-aware light transmission models and designing dynamically adaptive wavelet decomposition frameworks, in order to further enhance the preservation of fine textures and structural consistency under complex underwater conditions.  
Conceptualization, [PERSON] and [PERSON]; methodology, M.Z.; software, M.Z.; validation, M.Z., [PERSON] and J.X.; formal analysis, M.Z.; investigation, M.Z.; resources, [PERSON]; data curation, M.Z.; writing--original draft preparation, M.Z.; writing--review and editing, [PERSON], J.L., J.H., X.Z. and J.X.; visualization, M.Z.; supervision, J.X.; project administration, J.X.; funding acquisition, J.X. All authors have read and agreed to the published version of the manuscript.  
This research was made possible thanks to the generous financial support provided by various organizations. Specifically, we would like to acknowledge the Anhui Province Natural Science Foundation of China under grant number 2208085 US02.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the corresponding author on request.  
The authors are grateful to the three anonymous reviewers for their constructive comments.  
The authors declare no conflicts of interest.
# Modeling Non-Equilibrium Dynamics and Saturable Absorption Induced by Free Electron Laser Radiation
## 3 Results and Discussion
### Non-Equilibrium Dynamics in Al Thin Film  
In the previous section, we have shown that the three-channel model describes the saturation phenomena of X-rays of ultra short pulse well. In this part, we show the detail of the dynamics in the Al thin film during 60 fs. Figure 2 shows the results of time dependence of the density of occupation numbers normalized by the total number \(N\) at the end face of Al thin (53 nm) film. The fluences \(F\) are 1.5, 15 and 150 J/cm\({}^{2}\) for (a), (b) and (c), respectively. In the calculation, we estimated that the additional absorption by both sides of oxidized Al layers [7] reduces the pulse intensity by 70%. In the figures, the results obtained from the dynamical three-channel model is shown with solid lines, and static _long pulse_ model with the dotted line. In Figure 2a, we see that the results of the dynamical model tend asymptotically to reach the static limit. However the time of irradiation is not sufficient to reach those occupation number limits, which is around 10% of the population for the excited state. The dynamical model shows that the system is still highly populated by the ground state. In fact, this can be confirmed looking at Figure 1, where we see that the transmission of _long pulse_ model already shows a difference from the linear optical transmission, while the experimental and the dynamical ones are still well below the saturation threshold. For higher fluence at 15 J/cm\({}^{2}\), in Figure 2b, we see that the ratio of the occupation number of the excited state is much increased, such that the linear model (Lambert-Beer law) which works for the ground state fails. This failure is confirmed experimentally by the observation of growth of the transmission in Figure 1. In Figure 2c, we see that by increasing the pulse intensity the occupation of the ground state is strongly decreased, while most electrons are in the excited state. Under these conditions, the increment of the transparency and saturable absorption as the consequence of this are realized for this Al film. In this fluence range, a population inversion corresponding formally to negative temperatures (in equilibrium statistical mechanics terms) is realized. This phenomenon is associated with the existence of the relaxed state \(|3\rangle\), which does not participate in the photon absorption and simulated emission. In our calculations,  
Figure 1: XFEL pulse transmission for Al thin film (53 nm) at 92 eV photon energy. The green and blue solid lines indicate _long pulse_ model without (no rel.) and with an intermediate relaxed state; that is, the model of the green line is just a two-channel model. The pink solid line is for the _short pulse_ model. The light blue solid line corresponds to the three-channel dynamical model. The dynamical model agrees very well with the experimental (Expt.) data [7].  
the system returns to the ground state exponentially at the end of the short photon pulse (times larger than 60 fs in Figure 2).
# Ensemble Clustering in GPS Velocities: A Case Study of Turkey
## 2 Data and Methodology
### Generation Mechanisms
#### 2.3.2 K-Means Clustering  
The \(k\)-means method is a widely used clustering and unsupervised learning algorithm. In this method, first used by [PERSON] [35], the main objective is to partition \(k\) clusters from a database consisting of \(n\) data, in which each object belongs to the cluster with the nearest mean. By this means, the similarities within the cluster are homogeneous (maximized), and the similarities between clusters are heterogeneous (minimized) after the clustering process. In other words, the within-cluster variances are minimized in \(k\)-means clustering (Equation (12)).  
\[I_{k_{means}}=\sum_{j=1}^{k}\sum_{i=1}^{n}\
orm{x_{i}^{(j)}-c_{j}}^{2} \tag{12}\]  
where \(k\) is the number of clusters; \(n\) is the number of cases; \(x\) is the \(i\)th case \(c\) is the centroid for cluster \(j\).  
The process steps of the \(k\)-means clustering are as follows:  
* \(k\) _points are chosen randomly as initial cluster centroids._
* _The squared Euclidean distances from each point to the initially chosen centroids are calculated and then points are allocated to the nearest centroid._
* _The new cluster centroids of the formed clusters are updated by taking the mean of the points in each cluster._
* _The previous steps are repeated until the changes of all clusters remain stable and reach convergence. Notice that although it is possible to carry out \(k\)-means with other distance metrics such as Manhattan, Chebychev, etc., it is not suggested for they may prevent the convergence._
# Storm Surge Prediction Based on Long Short-Term Memory Neural Network in the East China Sea
## 4 Discussion
### Comparison with Other ML Methods  
As mentioned before, the most impressive feature of LSTM is the superior ability to deal with the time sequence data to traditional ML methods owing to its nature of long short-term memory. Hence, it is necessary to compare its performance with other traditional ML methods in terms of different prediction times. When training the four models with different prediction times, their ACC values of training set are all above 0.85 to guarantee the model precision. Figure 7 shows the model results of testing set for the BRR, GBDT, LR and SVR models with 1 h, 3 h, 7 h, and 15 h prediction times. For 1 h prediction results, it can be observed from Figure 7a,i that the line shapes of BRR and LR are quite similar with generally the same values of MAE, RMSE, ACC, and maximum difference (listed in Table 2), which is probably because BRR is a variant development of LR. Compared with BRR and LR, the morphology of GBDT and SVR for 1 h prediction result fits worse with the observation data. As the prediction time increases, the performance of the four models gets worse with different characteristics, i.e., the amplitudes of BRR and LR results are much smaller than the observation data and some parts of GBDT and SVR results are out of phase with the observation data and mix with noise.  
Figure 5: Model results of testing set for the LSTM model with (**a**) 1 h, (**b**) 3 h, (**c**) 7 h, and (**d**) 15 h prediction times. Red dash line circle represents the maximum difference between the modelled and observed values at the water level peak. Light blue areas are the cold start subsets for all prediction times.  
Figure 6: Model evaluation of testing set for the LSTM model with 1 h, 3 h, 7 h, and 15 h prediction times.  
decrease slightly from 3 h to 15 h prediction time. In Figure 8c, variation trend of ACC is on the contrary to the other evaluation metrics. In the four subplots of Figure 8, the evaluation values of LSTM (dark blue line) are separated from the other four models, which indicates that its prediction performance is visibly superior to the four traditional ML models. Taking ACC as the evaluation metric, the average ACC values of LSTM and the traditional ML methods for the four prediction times are 0.89 and 0.7, and therefore LSTM has a superior prediction ability by 27%.
## 4 Analytical Results
### Execution Time  
As mentioned in Section 2, it is necessary to evaluate whether the proposed method can cope well with model updating and processing significant amounts of traffic data, particularly regarding the computational load. Therefore, we measured the processing time using the time module in Python [24] when one- and two-stage clustering executed all the processes mentioned in Sections 4.1 and 3.2, respectively. The processing time includes dividing each traffic data into data pieces to extract unit traffic patterns for each unit of time for the 21 IoT traffic datasets.  
Figure 9(a) shows the processing time transition of one- and two-stage clustering with respect to the number of divided data pieces, thereby representing the granularity of the data division, which can be proportionally expressed as the reciprocal of the unit of time. When the granularity was less than 0.005, that is, when the unit of time was 300 s or larger, one-stage clustering required a shorter time than two-stage clustering. However, the processing time of the one-stage clustering rapidly increased as the granularity exceeded 0.005, whereas that of the two-stage clustering remained almost flat. From this perspective, the proposed two-stage clustering method has the advantage of increasing the amount of IoT traffic data that can be processed.  
Figure 10: Comparison of processing time for the one- and two-stage clustering. (**a**) Processing time of clustering vs. the granularity of data division. The increase in processing time for the one-stage clustering is much larger than the two-stage clustering. (**b**) Total processing time of clustering for all the six values of units of time. The two-stage clustering took approximately 61% less time than the one-stage clustering.  
Figure 9: Frequency distributions of the Euclidean distance of each traffic data piece from the corresponding cluster features derived from two-stage clustering.  
In this paper, we not only performed clustering but also created the combined time series representation of IoT traffic data using six values of units of time to grasp the more specific time variability properties of IoT traffic. The total time to perform clustering six times while varying the unit of time from 30 to 900 s is thus more important. Figure 10b shows the total time spent when executing the one- and two-stage clustering methods. The two-stage clustering took approximately 61% less time than the one-stage clustering. Therefore, even in terms of the total processing time, the proposed two-stage clustering outperformed the one-stage clustering.
# Position Accuracy Improvement by Implementing the DGNSS-CP Algorithm in Smartphones
## 4 Experiments and Results
### Static Test
The RMS of the horizontal and vertical error, 3.0 m and 2.3 m, respectively, has been reduced to 1.2 m and 1.3 m, as summarized in Table 3. The horizontal bias of 2.8 m has decreased to 0.7 m, and the maximum horizontal error of 3.7 m is now bounded at 2 m. At 199827 GPSTime, the RS generates the DGNSS corrections for all the visible satellites (GPS 4, 7, 8, 9, 11, 16, 21, 23, 26, 27, 30, GLONAS 10, 11, 20, 21, 22, 23, and Beidou 1, 2, 3, 4, 5, 7, 8, 10, 11, 12, 15), and based on the GSV messages V10 can select PRCs for the observed satellites (GPS 7, 8, 9, 11, 16, 21, 23, 26, 27, 30, GLONAS 10, 11, 21, 22, 23, and Beidou 1, 3, 4, 5, 10, 13) among the received PRCs. The smartphone then projects the set of range-domain PRCs to the position-domain using the modified DGNSS-CP algorithm to get a coordinate shift vector. As a result, the 3.3 m vertical error decreases to \(-0.2\) m.  
Figure 10: Zero-baseline static test results of the Galaxy S5 ((**a**) Horizontal error; (**b**) Vertical error). Figure 9: Original PRC in RTCM v.2 (**a**) and compensated PRC (**b**).
## 2 Materials and Methods
### Evaluation Metrics  
To validate the effectiveness of the proposed method, we use several evaluation metrics--accuracy, precision, F1 score, and Intersection over Union (IoU)--to quantitatively assess the performance of the person image segmentation model in search and rescue scenarios. The formulas for these metrics are provided in Equations (3)-(6).  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{3}\]  
\[Precision=\frac{TP}{TP+FP} \tag{4}\]  
\[F1-\text{Score}=\frac{2\times TP}{2 TP+FP+FN} \tag{5}\]  
\[IoU=\frac{TP}{TP+FP+FN} \tag{6}\]  
In the formulas, the terms are defined as follows:  
\(TP\): Number of pixels classified correctly as personnel.  
Figure 10: Picture annotation example diagram.  
Figure 9: Examples from the dataset.  
_TN_: Number of pixels classified correctly as background.  
_FP_: Number of background pixels incorrectly classified as personnel.  
_FN_: Number of personnel pixels incorrectly classified as background.
# Video Description Model Based on Temporal-Spatial and Channel Multi-Attention Mechanisms
## 4 Experiment
### Experiment Setting  
In our experiment, we sampled uniformly and set 26 frames as an interval group in a video. If a video was less than 26 frames, then we would fill the video with \"zero-pad\". For the corpus in the dataset, we used the NLTK (Natural Language Toolkit) [27] to tokenize and process the sentence. When word frequency is too low, it is difficult for the model to learn. At the same time, according to our statistics, the proportion of words with a word frequency of less than 4 is relatively small in the entire data set. For example, this proportion is only 1.69% in the MST-VTT data set. There are still some misspelled words or words with no specific meaning in the dataset, which are all incorrect words. The presence of these incorrect words will make it difficult for the model to describe the content of the video. Thus, words with a frequency less than four or incorrect would be eliminated. This is also a general processing method in video description [28]. Meanwhile, three specific words (<start>, <end>, and <unknown>) are added to represent the beginning of the model, the end of the model, and the unknown identifier, respectively. Finally, the word dictionary is obtained. For each description sentence, we would fix the length of the sentence to 20. If the length of the sentence were less than 20, then we would fill it with ending characters.  
The Inception-V3 [29] served as a CNN for extracting video features in the model. We used the output of \"pool3\" in the Inception-V3 as the video features with a dimension of \(8\times 8\times 2048\). We only used the Inception-V3 to extract video features, and we did not train the model parameters whose input was \(229\times 229\). The word representation used in our model is word embedding. In this model, the dimension of the hidden layer of LSTM was 1024, the word vector dimension of words was 512, and the time steps of LSTM were 20. The [PERSON] [30] algorithm is utilized to optimize the model training process with an initial learning rate of 0.001.
# Integrated Optical Phased Arrays for Beam Forming and Steering
###### Abstract  
Integrated optical phased arrays can be used for beam shaping and steering with a small footprint, lightweight, high mechanical stability, low price, and high-yield, benefiting from the mature CMOS-compatible fabrication. This paper reviews the development of integrated optical phased arrays in recent years. The principles, building blocks, and configurations of integrated optical phased arrays for beam forming and steering are presented. Various material platforms can be used to build integrated optical phased arrays, e.g., silicon photonics platforms, III/V platforms, and III-V/silicon hybrid platforms. Integrated optical phased arrays can be implemented in the visible, near-infrared, and mid-infrared spectral ranges. The main performance parameters, such as field of view, beamwidth, sidelobe suppression, modulation speed, power consumption, scalability, and so on, are discussed in detail. Some of the typical applications of integrated optical phased arrays, such as free-space communication, light detection and ranging, imaging, and biological sensing, are shown, with future perspectives provided at the end.  
integrated; optical phased arrays; beam shaping; beam steering +
Footnote †: journal:
## 4 Discussion
### Microbial Diversity Uncovered by the Application of Molecular Approaches
#### 4.2.1 Discovered Microbial Biodiversity on CHL  
As evidenced by this article, fungi and algae were mainly detected using CD methods, while archaea and bacteria were detected using CI methods. The identification of cyanobacteria was conducted with a relatively balanced employment of both kinds of techniques, while the identification of lichens was based on morphology and rarely employed molecular methods. Our results show that there were only two studies on lichen identification carried out using molecular approaches, one with CD [22] and one with CI methods [6]. Among the fungi in the cited articles (Table 1), which are also considered to play one of the major roles of limestone biodeterioration, _Cladosporium cladosporoides_ is the most common fungal species found in biodeteriorated limestone, having been detected and identified using culture-dependent approaches. However, _C. cladosporoides_ presents a complex of more than 200 species [62]. As this is obviously an important limestone colonizer, future research needs to focus on identifying the phylogenetic species involved. Algae were determined mainly by the use of CD methods. The most frequently found algae in the cited articles (Table 1) were _Acutodesmus bajacalifornicus_ and _Pseudosticococcus monallantoides_. The bacterial taxa in the cited articles (Table 1) were identified mainly by using CI methods. _Arthobacter agilis_ was the most frequently identified species in limestone. Cyanobacterial biodiversity was identified using CD and CI methods (Figure 6A,B). As noticed by [PERSON] et al. [17] in the cyanobacterial community in cultural heritage stone biofilms in six different tropical and subtropical countries, statistically significant differences were calculated according to the type of stone (calciferous or siliceous), as well as according to the climatic regions and atmospheric pollution. This was the first meta-analytical study performed on stone biofilm data obtained using the same sampling, isolation, and identification techniques. Regarding the archaea that are usually considered difficult to grow on culture media, the only species able to grow on axenic culture was _Haloferrax mediterannei_.
## 3 Mathematical Model  
The proposed mathematical model incorporates three main steps: establishing the existence of correlation for a time series of consecutive position measurements, computing the degree of correlation, and defining a model for positioning errors that incorporates correlation of successive measurements. In the following sections we detail each of the aforementioned steps.
## 3 Simulation Results and Discussion  
### Verification of Numerical Model  
To verify the basic property of mechanics, a RVC reinforced by a 10% volume fraction of identical circle particles is generated through the developed Matlab program. The uniaxial compressive load along the \(y\)-axis is applied, and the strain rate of applied load in this paper is fixed to be 0.0005/s. Figure 3A shows the nominal stress-strain response of the MGMC and pure BMG model, and Figure 3B show the distribution of equivalent plastic strain in BMG corresponding to point (I-IV) in Figure 3A. It can be seen from Figure 3 that the stiffness of the MGMC is a bit higher than that of the BMG, and obviously, the MGMC has better ductile property than pure BMG. Furthermore, there are four distinguishable stages in the process of the composite deformation, which is different from that of the BMGs. In first stage from the loading start to point (I), there is only elastic deformation in both the BGM matrix and Ta particles. In the second stage from point (I) to point (II), Ta particles enter the plastic deformation, while BMG is still in an elastic state. In the third stage from point (II) to point (III), both the matrix and particles deform with plastic strain. However, the response compressive stress is kept nearly as a constant, which is different from the steep descent of a pure BMG specimen. The main shear band in BMG is blocked by soft particles because plastic deformation is \"absorbed\" by them. The steep stress fall occurs in the fourth stage from point (III) to point (IV). The main shear band penetrates whole RVC, and both the particles and the matrix slide along it. As a result, the MGMC loses the load capacity.