esa-sceva/satcom-chunk-collection huggingface dataset:
# Source/Load-Pull Noise Measurements at K\({}_{\text{a}}\) [PERSON]
## 2 Materials and Methods
### Precautions for Accuracy  
As observed in [23], it is crucial that the parameters of all blocks remain stable during the measurement. For this reason, all passive blocks comprising flexible cables are kept mechanically fixed from their characterization onward. As to the variation of the DUT's source reflection coefficients, this is accomplished by controlling electro-mechanical switches as opposed to a tuner, to ensure repeatability. Clearly, the actual reflectances are measured directly at section _s-D1_, with the same on-wafer calibration used to measure the DUT.  
Instabilities of the DUT's operating point are minimized by fixing its drain voltage and tuning the gate voltage so as to obtain a desired drain current. After selecting one of the source terminations and before sweeping in frequency the relevant noise measurement, the gate voltage is retuned and the DUT's scattering parameters remeasured. Also, every frequency point of the noise measurement is preceded by a retuning of the gate voltage. To ensure consistency of the bias points between forward and reverse measurements, 4-wire DC measurements are performed at the end of the cables connecting the supply/measurement unit (SMU) and the RF-decoupled port of the bias-T's. Then, the residual resistances of the bias-T's are measured and accounted for.  
As to the receiver, this is made up of a pair of pre-amplifiers and a spectrum analyzer. The pre-amplifiers are necessary to increase the sensitivity of the whole receiver, but their total gain should be limited, both to avoid saturation and to limit gain instability. To remedy the instability of the receiver, the spectrum analyzer is made to auto-calibrate before each frequency sweep. Moreover, each noise power reading is accompanied by two readings for calibration. Also, each cycle of the DPDT switch is actually exploited for two sets of readings at the same frequency (calibration-cold, calibration-hot, measurement-cold, measurement-cold, calibration-cold, calibration-hot).  
Ambient temperature is kept as constant as possible by air conditioning of the test laboratory. Contact of the probes is safeguarded from vibration by means of an anti-vibration table.  
Finally, the resolution bandwidth (RBW) of the spectrum analyzer is limited to 0.8 MHz to ensure that the DUT's source reflection coefficient, \(\Gamma_{S}\), remains sufficiently constant across the RBW itself. In this regard, \(\Gamma_{S}\) exhibits an angular variation with frequency of approximately 1 deg/MHz, as mainly due to the length of the cable between the SPMT and the input bias-T. As to the video bandwidth (VBW), it is set to 20 Hz to improve the stability of the readings. As a further measure, 101 point traces are acquired and averaged at each reading.
## 3 System Model
### Satellite Coverage and Transmission Model
#### 3.3.2 GEO Data Relay Satellite Transmission Model  
Due to the limited transmission capacity of the LEO communication satellite, it cannot meet the user's all-weather real-time transmission requirements. Therefore, the relay transmission mode of the GEO data relay satellite and LEO satellite will become an important part of the SIN [30].  
We assume that the LEO satellite contains \(I\) tasks. Each task is arranged in descending order of importance. Task \(i\) represents the important task of the \(i\)-th item. The request rate of task \(i\) at time \(t\) is  
\[\lambda_{i}(t)=\frac{\varpi}{\rho i^{\alpha}}. \tag{15}\]  
The arrival process of task \(i\) obeys Poisson distribution with a \(\varpi\) parameter. The content of task request satisfies Zipf-like distribution. The probability of task \(i\) is \(1/\rho i^{\alpha}\), where \(\rho=\sum_{i=1}^{I}1/i^{\alpha}\), \(\alpha\) is the Zipf slope, and \(0<\alpha\leq 1\)[31].  
We are not sure if task \(i\) requires the transmission of a GEO data relay satellite. Therefore, we assume that task \(i\) is transmitted by the GEO relay satellite as a random variable \(\wp_{i}\). If task \(i\) does not require relay satellite transmission, then \(\wp_{i}=0\); otherwise, \(\wp_{i}=1\), constituting a Markov chain model \(\wp_{i}=\{0,1\}\) with two states. The transmission state of time \(t\) can be expressed as \(\wp_{i}(t)\), \(t\in\left\{\,0,1,2,\ldots,T-1\right\}.\) According to a certain transition probability, the transmission state \(\wp_{i}(t)\) is transferred from one state to another state. Let \(J_{\overline{S21522}}(t)\) denote the probability of transition from state \(\overline{S21}\) to state \(\overline{S22}\); then, the transition probability matrix \(\diamond_{i}(t)\) is obtained as follows:  
\[\diamond_{i}(t)=\left[J_{\overline{S21522}}(t)\right]_{2\times 2}, \tag{16}\]  
where \(J_{\overline{S21522}}(t)=\Pr\!\left(\wp_{i}(t+1)=\overline{S22}\middle|\wp_{ i}(t)=\overline{S21}\right)\), \(\overline{S21},\overline{S22}\in\wp_{i}\)[32].
# A New Resource Allocation Protocol for the Backhaul of Underwater Cellular Wireless Networks
## 2 System Description  
We consider a UCWN as shown in Figure 1, where there is one UBSC and multiple UBSs. Each UBS communicates with many UTs (or underwater nodes) in its cell. UTs collect surrounding information and transmit the information to the UBS. We also consider a special type of UT such as AUV which moves from one cell to another. This is connected with a UBS, and transmits much larger volume of traffic than normal UTs. In other words, we consider two types of UTs: the first type is just normal fixed UTs like sensors, which transmit a small amount of data in periodic manner, the second type is a mobile UT like an AUV, which transmits a much larger amount of data. We assume that there are a large number of first type UTs and a few second type UTs.  
In a UCWN, a UBSC is connected with many UBSs through underwater wireless backhaul as illustrated in Figure 1. There are two communication links: one is the link from the UBSC to the UBS (called downlink), and the other is from the UBS to the UBSC (called uplink). We can assume that uplink has much more traffic than downlink and thus requires much larger backhaul capacity than downlink. This is a reasonable assumption because a UBSC mostly transmits control signaling to UBSs. However, a UBS collects data from many UTs and transmits the data to the UBSC. This motivates us to design an efficient resource allocation protocol for the uplink backhaul in this UCWN throughput this paper.
## 4 Balancing Storage in DCS Systems
### Dynamic DCS  
The authors of [27] propose to use a dynamic GHT that is based on two main features: (i) A temporal-based GHT, that changes the home node over the time. (ii) A location selection scheme that chooses as potential home nodes those with more memory and battery resources. They say that with these two improvements the sensornet lifetime will be significantly extended.  
The authors focus the problem in node storage capacity, indicating that the home node of an event type could run out of memory, so that it could not be able to store new events. For that, they divide the network into grid cells. First of all they assume that there is one home node per event type that is allocated in the center of a cell. Then, they propose a hash function that permits allocating the home nodes of different event type in different cells. In addition, this hash function has into account a time slot \(  
time slots. After that time, it moves to the cell with the immediately lower identifier.  
In addition, the authors try to solve the problem of selecting the node with more resources in a particular cell as the home node. They use the Node Contribution Potential [29] as a measurement of the node capacity. In particular, this value is a combination of the storage capacity of the node and its remaining battery. Then, all the nodes inside a cell exchange their contribution potentials in order to calculate the cell potential, which is sent to the sink together with the location of the node with higher contribution potential within the cell. In turn, the sink selects a set of cells above a given cell potential threshold to be the location set for an arbitrarily long period, and broadcasts this set, together with the coordinates associated with it. During that period all nodes use that location set. Therefore, being \(n_{L}\) the number of cells in the set, the new hash function is:  
\[h(e_{i},t)=(te+i-(t\;div\;\vartheta))\;mod\;n_{L}\]  
This paper enables changing the home node for a given event type over the time. Thus, the authors claim that this alleviates the hot spot storage problem. However, they do not explain what happens with the already stored data during the transition from the old home node to the new one, thus it seems that the data stored in the old home node is lost. Furthermore, practical issues like the number of cells to be used, how they are assigned with an identifier, and how the sensors are able to know the identifiers of each cell, are not discussed.
# Representing Deep Neural Networks Latent Space Geometries with Graphs
## 3 Methodology
### Graph Signal Processing  
As mentioned in the introduction, graphs are ubiquitous objects to represent relationships (called edges) between elements in a countable set (called vertices). In this section, we introduce the framework of Graph Signal Processing (GSP) which is central to our proposed methodology. Let us first formally define graphs:  
**Definition 4** (graph).: _A graph \(\mathcal{G}\) is a tuple of sets \((\mathbb{V},\mathbb{E})\), such that:_  
1. _The finite set_ \(\mathbb{V}\) _is composed of vertices_ \(v_{1},v_{2},\ldots\)_._
2. _The set_ \(\mathbb{E}\) _is composed of pairs of vertices of the form (_\(v_{i}\)_,_\(v_{j}\)_) called edges._  
It is common to represent the set \(\mathbb{E}\) using an edge-indicator symmetric adjacency matrix \(\mathcal{A}\in\mathbb{R}^{|\mathbb{V}|\times|\mathbb{V}|}\). Note that, in this work, we consider only undirected graphs corresponding to symmetric \(\mathcal{A}\) (i.e., \((v_{i},v_{j})\in\mathbb{E}\Leftrightarrow(v_{j},v_{i})\in\mathbb{E}\)). In some cases, it is useful to consider (edge-)weighted graphs. In that case, the adjacency matrix can take values other than 0 or 1.  
We can use \(\mathcal{A}\) to define the diagonal **degree matrix \(\mathbf{D}\)** of the graph as:  
\[D_{i,j}=\left\{\begin{array}{cc}\sum_{j\in\mathbb{V}}\mathcal{A}_{i,j^{ \prime}}&\text{if }i=j\\ 0&\text{otherwise}\end{array}\right.. \tag{2}\]  
In the context of GSP, we consider not only graphs but also graph signals. A graph signal is typically defined as a vector \(\mathbf{s}\). In this work, we often consider a set of signals \(\mathbb{S}\) jointly. We group the signals in a matrix \(\mathbf{S}\in\mathbb{R}^{|\mathbb{V}|\times|\mathbb{S}|}\), where each of the columns is an individual graph signal \(\mathbf{s}\). An important notion in the remaining of this work is that of graph signal variation.  
**Definition 5** (Graph signal variation).: _The total variation \(\sigma\) of a set of graph signals represented by \(\mathbf{S}\) is:_  
\[\sigma=\text{tr}(\mathbf{S}^{\top}\mathbf{L}\mathbf{S}), \tag{3}\]  
_where \(\mathbf{L}=\mathbf{D}-\mathcal{A}\) is the combinatorial Laplacian of the graph \(\mathcal{G}\) that supports \(\mathbf{S}\), and \(\text{tr}\) is the trace function. We can also rewrite \(\sigma\) as:_  
\[\sigma=\text{tr}(\mathbf{S}^{\top}\mathbf{L}\mathbf{S})=\sum_{i,j\in\mathbb{V}}\mathcal{A }_{i,j}\sum_{\mathbf{s}\in\mathbb{S}}(\mathbf{s}_{i}-\mathbf{s}_{j})^{2}\, \tag{4}\]  
_where \(\mathbf{s}_{i}\) represents the signal \(\mathbf{s}\) defined on vertex \(v_{i}\). As such, the variation of a signal increases when vertices connected by edges with large weights have very different values._
# ReliaMatch: Semi-Supervised Classification with Reliable Match
## 4 Experiments
### Experimental Results and Analysis
The feature filtering module can select high-quality features, while the pseudo label filtering module can remove low-confidence pseudo labels. Their combined effect can improve model performance. Figure 5: Comparison of test error rates of different methods using different amounts of labeled data. (**a**) CIFAR-10. (**b**) SVHN. \begin{table}
\begin{tabular}{c c} \hline
**Method** & **Test Error Rate (\%)** \\ \hline w/o Feature Filtering & 6.74 \\ \hline w/o Pseudo-label Filtering & 9.12 \\ \hline w/o Feature Filtering and Pseudo-label Filtering & 16.9 \\ \hline ReliaMatch (benchmark) & 5.86 \\ \hline \end{tabular}
\end{table} 
Table 3: Exploring the influence of important modules of ReliaMatch on classification results. We demonstrated the training process of ReliaMatch using the CNN-13 network on the CIFAR-10 and SVHN datasets. ReliaMatch adopts a curriculum learning training approach. The method combines the selected samples and their reliable pseudo-labels with the labeled data, gradually increasing the difficulty of the training data to participate in model training in a supervised manner. As shown in Figures 6 and 7, during the training process, ReliaMatch reinitializes the model training after each label transfer to mitigate the problem of confirmation bias caused by pseudo-labels. **Discussion**. The semi-supervised classification method ReliaMatch solves the problems of underutilizing relationship between labeled and unlabeled data as well as introducing unreliable information into the model, and ReliaMatch also achieves desirable classification results in different networks and dataset application scenarios. Although ReliaMatch performs well in image semi-supervised classification issues, there are still some potential limitations: (1) The algorithm's performance depends on the confidence threshold. The confidence threshold needs to be well-designed and controlled in both feature filtering module and the pseudo-label filtering module to get the best results. (2) Additionally, ReliaMatch may also be affected by the selection of model's feature extraction and model types. Therefore, in future work, it can be considered to adaptively select appropriate confidence thresholds and hyper-parameters to improve and optimize ReliaMatch, so as to further enhance the existing methods and provide insights for future semi-supervised learning tasks.
###### Abstract  
Climate change has a significant impact on vegetation development. While existing studies provide some insights, long-term trend analysis and multifactor driver assessments for China are still lacking. At the same time, research on the future vegetation development under different climate change scenarios needs further strengthening. In response to these issues, this study analyzed China's normalized difference vegetation index (NDVI) data from 2001 to 2023, exploring vegetation cover trends, driving factors, and predicting the impact of future climate change. Firstly, this study decomposed the time series data into seasonal, trend, and residual components using the Seasonal-Trend decomposition using Loess (STL) decomposition method, quantifying vegetation changes across different climate zones. Partial least squares (PLS) regression analysis was then used to examine the relationship between NDVI and driving factors, and the contribution of these factors to NDVI variation was determined through the variable importance in projection (VIP) score. The results show that NDVI has significantly increased over the past two decades, especially since 2010. Further analysis revealed that vegetation growth is primarily influenced by soil moisture, shortwave radiation, and total precipitation (VIP scores > 0.8). Utilizing machine learning with Coupled Model Intercomparison Project Phase 6 (CMIP6) multimodel data, this study predicts NDVI trends from 2023 to 2100 under four emission scenarios (SSP126, SSP245, SSP370, SSP585), quantifying future meteorological factors such as temperature, precipitation, and radiation to NDVI. Findings indicate that under high-emission scenarios, the vegetation greenness in some regions may experience improved vegetation conditions despite global warming challenges. Future land management strategies must consider climate change impacts on ecosystems to ensure sustainability and enhance ecosystem services.  
NDVI; climate change impact; PLS regression models; CMIP6
## 3 Engineering Example
### Index System and Weight Determination  
This time, a number of second-level evaluation indicators are chosen underneath the first-level indicators to construct the evaluation index system. The foundation soil conditions, hydrogeological conditions, environmental geological problems, site stability, and underground space availability resources are chosen as the first-level evaluation indicators this time. This article divides the suitability of the environment and engineering construction and utilization in the new area into four grades, from poor suitability (a) to suitability (b), and analyzes the distribution characteristics of hydrogeological conditions, engineering geological conditions, and environmental geological problems in the study area and their impact on the development and utilization of above-ground and underground engineering in Xiongan New Area. Referring to pertinent data [2; 20] and experts' recommendations that empirical values establish grading standards, there are two ways to determine the extent to which various types of geological environmental impact factors will have an impact on project construction. Table 2 displays the quantitative grading standards for the evaluation factors.  
In this study, the subjective and objective integrated weighing approach and the entropy weight-analytic hierarchy process are used, which can effectively eliminate the drawbacks of employing the two methods independently. Through questionnaire surveys, in-person discussions, and other methods for weight evaluation and index assignment, as well as through the pertinent calculations to obtain the judgment matrix and the consistency test of the results, this evaluation arranges three groups of engineering construction suitability evaluation consultants, with each group consisting of ten people who are experienced engineering geology scholars. The evaluation index is weighted using the entropy approach. Five relevant experts from the consulting group create the initial matrix based on the scores of each index, and then the appropriate weighting computation is done. A calculation is made to determine the aggregate weight of each indicator, which displays how much of an impact each index has on the suitability of a project's construction (Table 3).
## 3 Results and Discussion
### Interferences and Selectivity  
The selectivity of the assay was investigated by comparing the net signal intensity obtained from Cys and binary mixtures of Cys with other common biomolecules and inorganic electrolytes typically found in biological fluids at concentrations equal or higher than their physiological levels [30]. Although the formation of complexes between amino acids and AuCl4- ions is well documented [31; 32], the bar plots in Figure 4 show that the recovery of cysteine in the presence of amino acids is higher than 95%, suggesting the lack of interference from amino acids, including the cationic amino acids arginine and lysine. We believe that the complex formation between AuBr4- with the amine group of CTAB, prior to sample addition, deters the complexation of free amino acids with Au anions. The only exception to this observation is cystine, which can be tolerated up to equimolar concentration levels to that of cysteine. The interference of cystine stems from the ability of Au(III) ions to oxidatively cleave the disulfide bonds of cystine, forming the respective sulfonic acid derivatives [33; 34]. This interference, however, poses no threat to the analysis because a reducing agent is commonly used before the analysis of thiols in order to reduce the disulfide bonds (oxidized thiols) to the more reactive sulfhydryl moieties (reduced thiols).  
Regarding inorganic electrolytes, they were found to influence the analytical signal, possibly due to the effect of counter-anions on the properties (e.g., micellization, Krafft temperature, surface tension, aggregation number, etc.) and structure of cationic micelles [26; 35]. Biological fluids contain a variety of inorganic electrolytes; therefore, we investigated the influence of ionic strength and inorganic electrolytes using artificial body fluids (for details see the experimental section). A calibration plot was then constructed in each matrix in order to study the effect of counter anions at various concentration levels of biothiols. Figure S2 shows that the artificial urine solution (AUS) had the lowest effect as compared to artificial blood plasma (ABP) while NaCl caused a significant suppression of the analytical signal. Therefore, dilution of the samples is necessary to reduce the relative abundance of inorganic electrolytes and thus minimize their effect on the properties of CTAB micelles. Technical aspects regarding sample pre-treatment are discussed further below in the analysis of biological samples.
## 3 Methods  
### Removal of Cloud Contaminated and Problematic Pixels  
A cloud screening procedure is among the indispensable processes for deriving SSTs from satellite thermal infrared imagery. In order to eliminate the cloudy or cloud-contaminated pixels upon SST retrieval, we applied a sequence of threshold tests based on the differences in radiation between sea surface and clouds according to their physical properties, as shown in Table 2[45; 46].  
In general, clouds are characterized by a broad, higher reflectance and lower temperature than the sea surface. The reflectance threshold test assumes that pixels with albedo exceeding 5%, as measured by the VIS channel data during daytime, are cloud-contaminated. A pixel with IR1 channel BT below \(-3.5\)\({}^{\circ}\)C was also eliminated for both daytime and nighttime in the gross cloud test. In the uniformity test, if the standard deviation or difference between the maximum and minimum of the both albedos and BTs within a window with 3 \(\times\) 3 pixels was above each threshold, that pixel was identified as cloud. Subsequently, an additional test for a thin cirrus was performed by giving dynamic thresholds as a function of the BT of IR1[47]. Although the VIS channel could not be utilized in the nighttime, a pixel considered as low stratus cloud was neglected using the BT difference between the SWIR and the IR2 channels. The threshold was determined depending on the BT of the IR1 channel [46]. The BT of the IR1 channel was compared with the maximum BT of IR1 channel for the previous 10 days to consider the difference in the temporal variation, as well as the spatial characteristics of the cloud and sea surface. Finally, we derived the SST fields and then compared them with the first-guess SSTs [48]. The pixels with obvious differences were contaminated by residual clouds. In addition to the cloud removal scheme, pixels with a sun glint angle of less than 15\({}^{\circ}\) and a satellite zenith angle (SZA) larger than 60\({}^{\circ}\) were removed from the matchup database to eliminate potential errors [13; 49].  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Criteria**} & \multicolumn{3}{c}{**Thresholds**} \\ \cline{2-3}  & **Day** & **Night** \\ \hline VIS & \textgreater{}5\% & - \\ BT\({}_{\text{IR1}}\) & \textless{}\textasci{}3.5\)\({}^{\circ}\)C & \textless{}\textasci{}3.5\)\({}^{\circ}\)C \\ STD VIS & \textgreater{}0.5\% & - \\ STD BT\({}_{\text{IR1}}\) & \textgreater{}0.7