esa-sceva/satcom-chunk-collection huggingface dataset:
# A Security-Enhanced Query Result Verification Scheme for Outsourced Data in Location-Based Services
## 2 Related Works
### Resistance to Dishonest Behavior  
How to prevent dishonest behavior in the cloud is also a current area of research. [PERSON] et al. [21] designed the continuous query KAT algorithm to prevent the cloud from analyzing the user's privacy based on the user's trajectory. By using this algorithm, the situation that the user's privacy may be compromised by the continuous query can be effectively solved. To further protect privacy, [PERSON] et al. [22] designed double-hidden regions in k-anonymity algorithms to prevent the cloud from accessing user privacy. In previous research, scholars have never really looked at the cloud as an attacker, to disrupt the entire LBS system, to examine the implementation options. However, in practical situations, especially when massive amounts of location data are increasingly and profoundly affecting people's lives, it is necessary to consider the cloud as an entity that threatens the security of the entire LBS system.  
Therefore, in this work, we use the Paillier cryptosystem and lookup table technology to guarantee storage and query privacy. We use verification objects to provide a fine-grained and reliable verification of fog nodes and outsourced data. Moreover, we use blockchain technology to effectively prevent dishonest behaviors of fog nodes.
## 3 Case Studies of Pilot and Full-Scale STRBs
### Water Works Sludge (England)
#### 3.4.3 Water Works Sludge Volume Reduction and Residual Sludge Development  
After each sludge load, dry solids accumulate on the bed surface as residual sludge, whereas most of the water content drains vertically through the residual sludge layer, and the residual sludge volume is also reduced via evapotranspiration. The sludge volume in an STRB is typically reduced at high levels due to the low solids content of the raw sludge. Monitoring of the loading phase over 5.5 months of the pilot STRB at the Whitacre WW showed that the sludge volume reduction reached 97-98%. As a result of the intensive feeding cycles with four days of loading, e.g., in Bed 3 from 23 to 27 th November (Figure 11), the residual sludge layer thickness increased fast, while it was markedly decreased thereafter during the resting period due to drainage and evapotranspiration [53].  
A similar reduction of the sludge volume (>98%) after 3-5 days of sludge application was observed at the Hanningfield pilot STRB. Both pilot and full-scale systems for WW sludge indicate that the ds content in the feed sludge (approx. 0.1-0.2%) is 100-200 times concentrated in the residual sludge layer, reaching a content between 25 and 40% [58]. A higher ds content (>50%) in the residual sludge was measured in some pilot beds at the Hanningfield WW.  
Furthermore, as already mentioned, the visual status of the residual sludge layer serves as another indication of the sludge dewaterability. After sludge application, the WW sludge surface quickly shows cracks (Figure 12) as a result of the low fat and oil and organic matter contents. On the other hand, some types of sewage sludge will not crack so easily, especially sludge from WWTPs receiving dairy or abattoir wastewater with a high fat and oil content.  
Figure 10: Capillary suction time (CST) of surplus activated sludge (blue X) and sludge from the Hanningfield WW (red dots) and Whitacre WW (blue dots) [53].
## 2 Detection Method of Microseisms in Coal Mines
### Extraction of Temporal and Spatial Characteristics of Coal Mine Microseisms Based on Markov Chain
#### 2.1.3 Extraction of Time Characteristics of Microseismic Signal of Target Node Coal Mine  
The sensor data at the adjacent time have a time correlation, meeting the requirements of the Markov chain in \(t\)'s state of the moment being the same as \(t-1\). Therefore, this paper constructed the target node state sequence after state mapping into the form of the Markov chain and extracted the time characteristics of the coal mine microseisms at the target node by calculating the state transition probability matrix. The calculation method of the state transition probability matrix is as follows.  
Let the first-order Markov chain model established by the target node state sequence be \(T=\{T_{1},T_{2},\cdots,T_{t}\}\) and the state space be \(S=\{a,b,c,d,e,f,g,h,i\}\), and for all \(s_{1},s_{2},\cdots\), \(s_{t}\in S\), as well as \(t\in\{1,2,\cdots\}\), all meet:  
\[P\{T_{t}=s_{t}|T_{0}=s_{0},T_{1}=s_{1},\cdots,T_{t-1}=s_{t-1}\}=P\{T_{t}=s_{t }|T_{t-1}=s_{t-1}\} \tag{3}\]  
Order \(s_{i}\) represents model \(T\) at the moment of \(t-1\)'s state; \(p_{ij}\) represents model \(T\) at the moment of \(t\)'s state, the transition probability of \(s_{j}\); the calculation formula of \(p_{ij}\) is as follows:  
\[p_{ij}=P\big{(}s_{j}|s_{i}\big{)}=\frac{P\big{(}s_{0}s_{1}\cdots s_{i}s_{j} \big{)}}{P(s_{0}s_{1}\cdots s_{i})}=\frac{P\big{(}s_{j},s_{i}\big{)}}{P(s_{i}) }\Rightarrow p_{ij}=\frac{N\big{(}s_{0}s_{1}\cdots s_{i}s_{j}\big{)}}{N(s_{0} s_{1}\cdots s_{i})}=\frac{N\big{(}s_{i},s_{j}\big{)}}{N(s_{i})} \tag{4}\]  
where \(N\) indicates the total number of times the status occurs.  
The state transition probability of the target node state sequence in state space \(S=\{a,b,c,d,e,f,g,h,i\}\). The size of the inner component is a \(9\times 9\) state transition probability matrix of \(P\):  
\[P=\left(\begin{array}{cccc}p_{aa}&p_{ab}&\cdots&p_{ah}&p_{ai}\\ p_{ba}&p_{bb}&\cdots&p_{bh}&p_{hi}\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ p_{ia}&p_{ib}&\cdots&p_{ih}&p_{il}\end{array}\right) \tag{5}\]where \(p_{ij}\geq 0\) and \(\sum\limits_{s_{i}s_{j}\in S}p_{ij}=1\).
## 1 Introduction
Therefore, a joint satellite-transmitter and ground-receiver digital pre-distortion (JSG-DPD) technique, by reusing the ground-receiver as the \"feedback loop\", is proposed in this paper to achieve linearization of the space-borne APA. It is experimentally demonstrated that this technique can dramatically save space-borne resources and can achieve a promising 7.5% EVM improvement. To the best of our knowledge, this paper is the first attempt to join the  
Figure 1: Block diagram of DPD structure of conventional two categories of array antennas: (**a**) one-to-one DPD; (**b**) overall DPD. receiver and transmitter for DPD in order to obtain desirable results with less cost. With the future deployment of a large number of LEO satellite systems, the search for effective high-power-efficient satellite communication techniques becomes increasingly urgent. The rest of the paper is organized as follows: Section 2 presents the main principles of the proposed technique. Section 3 explains the algorithm for calculating the DPD coefficients of the GMP model. Section 4 shows the experimental results and analysis. Conclusions are given in Section 5.
## 2 Methodology
### Transformer Branch  
The design of the Transformer branch follows the codec structure. As shown in Figure 1, an image with a size of \(x\in R^{H\times W\times C}\) is divided into \(N=\frac{H}{S}\times\frac{W}{S}\) patch blocks of equal size in the Transformer branch, where \(S\) is usually set to 16. After the patch blocks are divided, they are flattened and passed to the linear embedding layer with an output dimension of \(D_{0}\), and the original embedding sequence \(e\in R^{N\times D_{0}}\) is obtained. To exploit spatial prior information, a learnable positional embedding of the same dimension is added under \(e\) to generate \(e\) with positional embeddings \(z^{0}\in R^{N\times D_{0}}\). The Swin Transformer improves the attention mechanism, as shown in the following formula:  
\[Attention(Q,K,V)=SoftMax\Big{(}QK^{T}/\sqrt{d}+B\Big{)}V \tag{4}\]  
Figure 2: Fuzzy block structure visualization.  
where \(Q,K,V\in R^{M^{2}\times d}\) are the query, key, and value matrices, and \(d\) is the dimension. \(B\in R^{M^{2}\times M^{2}}\) is the relative position bias, which determines the relative position of each token in the coding window, \(M\) is the size of the window, and there are \(M^{2}\) tokens in total. By defining a training bias matrix \(\hat{B}\in R^{(2M-1)\times(2M-1)}\), \(B\) can be obtained through indexing.  
The improvement strategy of the Swin Transformer V2 involves using continuous relative position bias, no longer defining a fixed size, but using a small network to predict relative position bias, as shown in the following formula:  
\[B(\Delta x,\Delta y)=G(\Delta x,\Delta y) \tag{5}\]  
where \(G\) is an MLP model containing 2 layers with ReLU activation in the middle. The advantage of using network \(G\) is that it can generate relative position bias at any relative position so that no changes are required when migrating to a larger window. Although network \(G\) can adapt to windows of different sizes, when the window size changes, the relative position range also changes, which means that network \(G\) has to accept a different input range than the pre-trained model. In order to minimize the change in the input range, we further propose replacing the original linear coordinates with the coordinates in the log space. The conversion formula of the two is as follows:  
\[\begin{split}\widehat{\Delta x}&=sign(x)\cdot \log(1+|\Delta x|)\\ \widehat{\Delta y}&=sign(y)\cdot\log(1+|\Delta y|) \end{split} \tag{6}\]  
where \(\Delta x,~{}\Delta y\) are the linear-scaled coordinates and \(\widehat{\Delta x},~{}\widehat{\Delta y}\) are the log-spaced coordinates.
# Robust Fingerprint Minutiae Extraction and Matching Based on Improved SIFT Features
## 5 Conclusions  
This paper has introduced an automated minutiae extraction and matching framework for fingerprint identification and verification purposes, The proposed framework has followed a stepwise procedure as follows: first, multiple preprocessing operations including denoising, binarization, thinning, dilation, and enhancement are performed on the input fingerprint image to obtain high accuracy minutiae data. An improved SIFT detector is then applied to high-contrast fingerprints to detect an optimized set of highly-reliable salient points discriminating fingerprint minutiae and describe them accurately and quickly. Then, the SIFT descriptors of the local key-points in a given fingerprint are matched with those of the stored templates using a brute force approach. Finally, an adaptive dual-threshold filter is applied to remove false matches, while preserving the correct one. Experimental results on the public FVC2004 fingerprint datasets have demonstrated that the presented framework admits highly competitive or even much better performance than several state-of-the-art methods in terms of EER and robustness. Furthermore, the results of the performance evaluation indicated that the framework is easy to perform, inexpensive, relatively fast, and can offer enough finger ridge detail to allow for excellent fingerprint comparison for identification and verification purposes, which can be deemed as the most important managerial implication emerging from this study. Nevertheless, a possible limitation of this study is that the validation process has been performed using a single database of a relatively small number of latent fingerprint images. Future work will be along two main axes. The first will be further extension of this framework by incorporating advanced deep learning paradigm for better recognition performance. The second will concentrate on conducting more experiments for testing and evaluation of our approach on challenging latent fingerprints unintentionally left by subjects at crime scenes.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l l} \hline
**Techniques** & **Elapsed Time (ms)** \\ \hline Proposed Technique & 674 \\ [PERSON] and [PERSON] [41] & 632 \\ [PERSON] et al. [42] & 745 \\ [PERSON] et al. [43] & 957 \\ [PERSON] et al. [44] & 1075 \\ [PERSON] et al. [45] & 1119 \\ [PERSON] [47] & 1149 \\ \hline \end{tabular}
\end{table}
Table 2: Average time taken comparison of various closely related techniques.
## 2 Special Features of Nanoparticles
### Particle size dependent properties of inorganic nanoparticles
Both ceramics were found to occur in non-typical phases as nanoparticles compared to the bulk material and are subject to phase transformation and grain growth with increasing temperature. Coating the nanoparticles with a different ceramic layer suppresses phase transformations and obstructs grain growth. The knowledge about which phase is stable under which conditions is in-so-far important, as TiO\({}_{2}\) is frequently used as filler to modify optical properties of polymers. The phases differ in their refractive indices: bulk anatase is characterized by a refractive index of 2.54 (at 550 nm) and a band gap of 3.20 eV, whereas rutile is characterized by a refractive index of 2.75 (at 550 nm) and a band gap of 3.03 eV for bulk, respectively. For amorphous thin TiO\({}_{2}\) films a refractive index of 2.51 (at 550 nm) and a band gap of 3.27 eV were reported [27]. Size effects regarding electrochemical properties and cycling stability were described for nanoscaled TiO\({}_{2}\)[28, 29]. With decreasing anatase particle size from 30 m to 6 nm, an increase of capacity was observed, indicating an improved lithium storage capability [28]. Similar effects were observed for rutile [29]. Here the authors found a significant increase in capacity with decreasing size from 300 nm to 15 nm for rutile particles. As both phases were cycled under different conditions, the results cannot be compared directly. [PERSON] _et al._[30] comment that anatase - among all different TiO\({}_{2}\) phases - presents the most interesting potential regarding electrochemical properties. Size effects for dielectric properties were found in a different size range. [PERSON] _et al._[31] observed a decreasing ferroelectric to paraelectric phase transition in PbZrO\({}_{3}\) at particles sizes below 100 nm, and a decrease in dielectric constant. These observations were in parallel with a decreasing pseudo-tetragonal distortion of the crystal lattice. [PERSON] _et al._ observed a particle size dependent existence of phases in BaTiO\({}_{3}\)[32]. Below a particle size of 70 nm the paraelectric phase was stable; above 100 nm the tetragonal ferroelectric phase was stable. [PERSON] _et al._ reported about a maximum dielectric constant in BaTiO\({}_{3}\) occurring at particle sizes of 70 nm or 140 nm, depending on particle synthesis method [33].
# On-Orbit Experimental Result of a Non-Deployable 430-MHz-Band Antenna Using a 1U CubeSat Structure
## 7 Analysis from Recorded IQ Signal and Discussion
### Estimates of the Gains Demonstrated in Space  
Neglecting the change of attitude, the gain of the Loop Hentenna, \(G_{l}\), can be obtained from the dipole antenna gain, \(G_{d}\), and the respective transmitter outputs, \(P_{txt}\), \(P_{txt}\) and the difference of the receiving signal strength, \(\Delta P_{rx}\), by the following equation:  
\[G_{l}=G_{d}-P_{txt}+P_{txt}-\Delta P_{rx}. \tag{2}\]  
As BIRDS-4 was not attitude controlled during the HNT mission, the attitude during the experiment is not known. Assuming that the gain of the dipole antenna is 3.2 dBi, as measured in the ground test, and all factors are the same except for the range distance and transmitter power, the Loop Hentenna gain \(G_{l}\) is calculated as \(-8.1\) dBi from \(P_{txt}\) = 17.3 dBm and \(P_{txt}\) = 13.0 dBm.  
This value is 2.4 dB lower than the value obtained during the ground test, but this is due to many uncertainties, such as attitude. From the housekeeping data, the satellite rotational speed was typically 0 to 5 deg/s. It was confirmed that at least the Loop Hentenna is functioning as an antenna in space. The stable signal shows that the radiation pattern is non-directional.  
The signal-to-noise ratio is at least good enough for listening and decoding the data. The number of missions that have been conducted is still low, so that needs to be increased to obtain more detailed data.  
Figure 19: Received signal at elapsed time 16 to 26 s.  
Figure 20: Received signal with elapsed time 56 to 61 s.
# FusionTrack: Multiple Object Tracking with Enhanced Information Utilization
## 3 Methodology
### Tracklet Management  
For most MOT models, a tracklet manager module receives the object candidates and corresponding appearance features from object queries and track queries. These inputs are used to update the states of all tracking tracklets and initialize newborn tracklets. Inspired by the end-to-end track-by-attention models, we propose a simple yet effective tracklet management scheme.  
In FusionTrack, a tracklet is represented using its class label, appearance features, bounding box, and confidence. The label remains unchanged during a tracklet's lifetime, while other features are updated during the tracking process. Following BOT-SORT [5], an additional group of kinematic states \(s_{k}=\{x,y,w,h,\Delta x,\Delta y,\Delta w,\Delta h\}\) representing the  
Figure 4: The structure of score-guided multi-level query fuser. An encoder-like structure is used to generate appearance features from different queries using score embeddings.  
bounding box position, size and their changing rate for each tracklet is maintained using the Kalman filter.  
In FusionTrack, the detections that overlap with the tracking targets are removed from all detection objects, and the remaining detections are used to initialize tracklets. Meanwhile, the predictions from track queries are directly used to update the tracklet states as MOTR [11] does. The detailed process is listed in execution order as follows:  
1. NMS and confidence filters are applied on detection and tracking predictions separately to remove poor results. This step is essential since duplicated prediction can result in a large number of false positives.
2. The IoU between the remaining detection and tracking results is computed, and detection boxes that overlap any track box with IoU higher than the threshold \(\theta_{IoU}\) are removed to avoid generating duplicate tracklets.
3. Remaining track predictions are used to update the states of tracklets.
4. Remaining detections are used to generate new tracklets.
5. Tracklets that have not been updated for more than \(k\) frames are removed from the active list.  
Compared with the traditional models that match track predictions with detections using complicated algorithms and additional reID module, the association in FusionTrack is done implicitly using bounding box information only. We claim that its effectiveness is a side effect of the query fusion module, where the track queries receive information from the detection branch to generate closer bounding box predictions.
## 3 Results and Discussion
### Trend and Correlation Analysis for Input Parameter
In addition, next-day PM\({}_{2.5}\) had a positive correlation with the variances of the air pollutants, and had a positive correlation with NO  
Figure 4: Daily average with 30 –day moving average for (**a**) Temperature, (**b**) Relative humidity, (**c**) Wind speed, (**d**) Atmospheric pressure, (**e**) Precipitation, and (**f**) Snow fall. (0.58), CO (0.46), and PM\({}_{2.5}\) (0.49). Based on the correlation analysis results, using daily variances of air pollutants as independent variables is, thereby, reasonable. Table 7 shows the correlation between next-day PM\({}_{2.5}\) and meteorological factors. In terms of the daily average, next-day PM\({}_{2.5}\) was positively correlated with atmospheric pressure (0.29) and snowfall (0.05). Temperature (\(-0.31\)), humidity (\(-0.21\)), wind speed \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline  & x1 & x2 & x3 & x4 & x5 & x6 & xv1 & xv2 & xv3 & xv4 & xv5 & xv6 & y \\ \hline x1 & **1.00** & **0.56** & **0.56** & **0.09** & **0.61** & **0.81** & **0.55** & **0.53** & **0.19** & **0.25** & **0.44** & **0.64** & **0.57** \\ x2 & **1.00** & **0.68** & **\(-\)0.38** & **0.85** & **0.69** & **0.07** & **0.90** & **0.16** & **0.10** & **0.75** & **0.45** & **0.62** \\ x3 & & & **1.00** & 0.03 & **0.58** & **0.61** & **0.08** & **0.69** & **0.55** & **0.33** & **0.58** & **0.35** & **0.52** \\ x4 & & & & **1.00** & **\(-\)0.41** & 0.02 & 0.00 & \(-\)**0.13** & **0.31** & **0.66** & \(-\)**0.49** & \(-\)0.03 & \(-\)0.02 \\ x5 & & & & & **1.00** & **0.79** & **0.12** & **0.69** & 0.01 & \(-\)0.04 & **0.87** & **0.60** & **0.63** \\ x6 & & & & & **1.00** & **0.20** & **0.63** & **0.20** & **0.31** & **0.58** & **0.78** & **0.71** \\ xv1 & & & & & & **1.00** & **0.06** & 0.01 & \(-\)0.01 & **0.10** & **0.27** & **0.14** \\ xv2 & & & & & & & **1.00** & **0.28** & **0.32** & **0.62** & **0.42** & **0.58** \\ xv3 & & & & & & & & **1.00** & **0.47** & 0.03 & **0.07** & **0.15** \\ xv4 & & & & & & & & & **1.00** & **-0.15** & **0.13** & **0.23** \\ xv5 & & & & & & & & & **1.00** & **0.50** & **0.46** \\ xv6 & & & & & & & & & & **1.00** & **0.49** \\ \hline \end{tabular}
\end{table} 
Table 6: Correlations among next-day PM\({}_{2.5}\) and air pollutants (nationwide daily average and variance). Figure 5: Daily standard deviation with 30\(-\)day moving average for **(a)** Temperature, **(b)** Relative humidity, **(c)** Wind speed, **(d)** Atmospheric pressure, **(e)** Precipitation, and **(f)** Snow fall. (\(-0.32\)), and precipitation (\(-0.26\)) were negatively correlated with next-day PM\({}_{2.5}\). In terms of daily variance, next-day PM\({}_{2.5}\) was positively correlated with temperature (0.40), humidity (0.30), and snowfall (0.02), and negatively correlated with the rest of the factors. In general, meteorological factors had lower correlations than air pollutant factors but were used for prediction model development as important independent variables showing seasonal changes.
## 4 Mathematical Model
### Assumptions  
The mathematical model for the scheduling of the food production process inherits its main assumptions from the standard flexible job shop scheduling problem with sequence-dependent setup times and additional features due to the job splitting:  
* Jobs are independent of each other,
* A job can be split into sub-lots,
* The sub-lots of a job can be grouped on the machines to be treated at the same time,
* Each sub-lot of a job consists of a set of operations that must be processed consecutively (precedence constraints between operations of sub-lots of jobs),
* Each operation of a sub-lot has a given processing time,
* The preemption of operations of sub-lots of jobs is not allowed, i.e., operation processing on a machine cannot be interrupted,
* Each job has a given due date (finish date of production at latest),
* Sub-lot sizes (number of portions) are discrete,
* Sub-lots creation is consistent throughout the processing sequence, meaning that job splitting and sub-lot sizes remain constant for all operations,
* Machines are independent,
* A machine can process, at most, one operation of a job at the same time,
* The setup times of machines are dependent on the sequence of operations of sub-lots of jobs,
* Material resources have given availability time windows that must be taken into account.  
Taking these assumptions into account, the objective is to find a schedule involving sub-lot assignments to machines and sub-lot sequencing for each machine in such a way that each job's demand is fulfilled, different constraints of the problem are respected, and the total flow time of jobs in the production system is minimized.