esa-sceva/satcom-chunk-collection huggingface dataset:
# Substring Position Search over Encrypted Cloud Data Supporting Efficient Multi-User Setup +
## 4 Substring Search Algorithms
### Discussion  
Recall that our goal is to construct a scheme that allows a substring search over encrypted data outsourced to the cloud. In our system model, the data owner has a set of documents with sensitive information that he wants to upload in encrypted form to the cloud provider. To enable the substring search, the data owner constructs a searchable index \(I\) from the set of plaintext documents \(D\), and then, he places \(I\) at the cloud provider to allow the substring search. Our goal is to choose an optimal data structure that has low storage requirements and fast search execution. Later, we use the selected data structure to construct the searchable index \(I\) in our scheme. As we have noted previously, many substring matching algorithms have been proposed, and they differ in terms of storage requirements and search execution. We outline the comparison of substring search data structures in Table 1. We use several comparison parameters: the construction time, the search execution time and the storage requirements. The construction time corresponds to the time it takes to create a data structure with the input of the text \(t\) of size \(n\). The search execution time is the time it takes to find all occurrences of substring \(\chi\) of length \(m\) in the text \(t\). The cloud storage describes a storage of all combined textual labels in each data structure. From Table 1, we can see that the suffix tree, suffix array and position heap tree have \(O(n)\) preprocessing time of text \(t\) of size \(n\). In search, the suffix tree has the best \(O(m+occ)\) execution time for a substring \(\chi\) of length \(m\). However, the suffix tree has at most \(2n\) nodes, and it would take \(O(n^{2})\) space to store the text at the cloud provider. On the other side, the suffix array can be constructed with \(n\) elements, but it has \(O(n^{2})\) storage. Only the position heap tree allows us to have the low storage \(O(n)\) with \(n\) nodes; however, the substring search execution takes \(O(m^{2}+occ)\) time. In the rest of the paper, we use the position heap tree data structure as the main construction block for our scheme. In our choice between the data structures, we believe that the \(O(n)\) storage requirement is the predominant criteria, since expanding any large dataset (e.g., the human genome with three billion letters) to a \(O(n^{2})\) storage would cause a substantial waste of cloud computing resources.
# Batch and Column Adsorption of Phosphorus by Modified Montmorillonite
## 2 Materials and Methods
### Column Adsorption Experiment in Elution Column  
In the column adsorption experiment, Al\({}_{13}\)-O-MMt adsorbent was used to treat 2000 mL of 200 mg/L phosphorus. As the results in the experiment on the effect of adsorbent dosage on phosphorus adsorption indicate that the optimal dosage of Al\({}_{13}\)-O-MMt is 0.1 g for the adsorption of 30 mL of 200 mg/L phosphorus, as was demonstrated in the batch adsorption experiment, the amount of Al\({}_{13}\)-O-MMt used in the column experiment could be calculated as follows:  
\[\text{Amount}=\frac{\text{total amount of phosphor in aqueous solution}}{ \text{adsorbed amount of phosphor by per g of Al}_{13}-\text{O}-\text{MMt}}\] \[\text{Amount}=\frac{\frac{200\text{ mg}}{1\text{ L}}\times\frac{1\text{L}}{1000\text{ mL}}\times 2000\text{ mL}}{\frac{200\text{ mg}}{1\text{ L}}\times\frac{1\text{L}}{001\text{ mg}}\times 30\text{ mL}}\approx 6.667\text{ g}\]  
A total amount of 6.667 g Al\({}_{13}\)-O-MMt was then weighed and placed in the column to treat 2000 mL of 200 mg/L phosphorus solution at pH 8. A 0.45 \(\upmu\)m filter membrane (Jinteng, Tianjin, China) was placed at the bottom of an elution column (Huida Laboratory Consumables, Chongqing, China).  
Next, 6.667 g of Al\({}_{13}\)-O-MMt was packed into the empty elution column and connected to a constant flow pump (KSP-F01A, Kamoer, Shanghai, China) with a flow rate of 0.417 mL/min (Figure 1). Afterwards, 200 mL of the phosphorus solution (200 mg/L) was added and drawn evenly across an 8 h period per day. The experiment spanned 12 days (2000 mL in 10 days was obtained from the prediction in the batch experiment, 400 mL in 2 days was used for error detection), and a total of 2400 mL of 200 mg/L phosphorus solutions was loaded into the column to ensure that the adsorbent was saturated. The operation duration for each day was 8 h. Moreover, 200 mL of sample was collected each day, and 12 samples were collected at the end of Al\({}_{13}\)-O-MMt adsorption after 12 days. The same method (GB 11893-89) as that used in the batch adsorption experiment was applied to determine the concentration of phosphorus in a daily sample of this column experiment.
## 2 Technological and Policy Solutions
### Policymaking Arenas
#### 2.2.2 ECAs  
International regional Emission Control Areas (ECAs) have been established in four areas, and they are all recognized by the IMO, specifically in the International Convention for the Prevention of Pollution from Ships (MARPOL) Annex VI. Two of the five are particularly noteworthy because they are international agreements that limit ships' black carbon emissions (as PM\({}_{2.5}\)). One is the large North American ECA, which covers the Atlantic and Pacific coasts [75]. The North Atlantic ECA was signed by Canada, the United States, and France, which has two territories (Saint Pierre and Miquelon off the Newfoundland Atlantic coast of Canada) and adopted by the IMO in 2010. The second ECA with PM\({}_{2.5}\)/black carbon limits is the US Caribbean ECA, which includes Puerto Rico and the US Virgin Islands, and which was recognized by the IMO in 2011 [76].  
There are two ECAs in northern Europe--one for the Baltic Sea and the other for the North Sea and part of the English Channel. However, these were only created as SECA's, i.e., Sulphur ECAs. They are scheduled also to be NECAs (Nitrogen ECAs) beginning in 2021. Whether either or both will add particulate matter, including black carbon, remains to be seen [77].
# Multi-Angle Fast Neural Tangent Kernel Classifier
## 5 Analysis of Experimental Results
### Comparison of Multi-Model Results
From Figure 2 and Table 3, it can be easily seen that our proposed multi-kernel-NTK model achieves higher computational efficiency in most datasets in different domains, saving up to nearly 60% of the time. At the same time, the model also improves the classification accuracy of the traditional NTK model, ranging from 1% to 25% in different datasets. For the BLOCK model, there may be a risk of deepening the time complexity in some datasets, but overall, the speedup is achieved in most datasets and the accuracy of the original model is relatively maintained.
## 3 Results
### 13-HPODE-treated PDiff Caco-2 Cells
#### 3.1.3 Pathway Analysis  
We found that treating PDiff Caco-2 cells with 13-HPODE led to upregulation of retinol metabolism, which could be a counter-regulatory defense mechanism against 13-HPODE, which is considered a xenobiotic. Consistent with previous reports [48], 13-HPODE induced steroid hormone biosynthesis, which has a role in maintaining intestinal barrier [49]. Additionally, starch and sucrose metabolism as well as amino acid metabolism were upregulated. Other pathways that were enriched with slightly higher \(p\) valuesincluded PPAR signaling as well as peroxisome pathway and metabolism of xenobiotics by cytochrome P450 suggesting that 13-HPODE could trigger detoxification mechanisms. On the other hand, ribosome, RNA polymerase, spliceosome, and pyrimidine metabolic pathways were suppressed due to 13-HPODE treatment. Phagosome degradation pathway was also downregulated. Moreover, 13-HPODE treatment caused downregulation of oxidative phosphorylation indicating possible impaired mitochondrial function (Table 1).  
Figure 2: Gene ontology (GO) analysis in 13-HPODE-treated poorly differentiated (PDiff) Caco-2 cells. Top enriched biological processes (**a**), molecular function (**b**), and cellular components (**c**) in 13-HPODE-treated cells relative to untreated (control) cells (adjusted \(p<0.05\)).
# Characterizations of Interfacial Solar Water Evaporation
## 3 Performance Evaluation
### Inductively Coupled Plasma Mass Spectrometry  
Inductively coupled plasma mass spectrometry (ICP-MS) is a technique based on ionizing samples with inductively coupled plasma and then detecting the resulting atomic ions. It is typically utilized for measuring metals and non-metals in liquid samples at low detectable limits. It is used in isotopic labeling because it can identify various isotopes of the same element. A nebulizer is used to turn a liquid into an aerosol, which is then transported to the plasma to be ionized. Ions produced in the argon plasma are then sent from the detector through the mass analyzer.  
[PERSON] et al. [37] created a wood-based 3D solar evaporator by controlling the hydrophilicity of a charred wood surface and altering the height of the wood above the water surface. They used ICP-MS to determine the amounts of principal ions Na\({}^{+}\), Mg\({}^{2+}\), K\({}^{+}\), and Ca\({}^{2+}\) in the original seawater before desalination and condensed water after purification. They found that desalination drastically decreased all four major ion concentrations. The Na\({}^{+}\) content was far below the limit for drinking water established by the World Health Organization and the US Environmental Protection Agency (Figure 12a). [PERSON] et al. [38] created an evaporator made of carbon extracted from bamboo leaves and used ICP-MS to show that the quantities of four principal ions (Na\({}^{+}\), Mg\({}^{2+}\), K\({}^{+}\), and Ca\({}^{2+}\)) in the treated water drastically reduced by nearly three orders of magnitude relative to the original seawater after desalination under irradiation of one Sun (Figure 12b).  
Figure 11: Heat loss. (**a**) Carbonized mushrooms. Reproduced with permission from ref. [9], Copyright 2017, Wiley-VCH. (**b**) 3D crosslinked honeycomb graphene foam material. Reproduced with permission from ref. [36], Copyright 2018, American Chemical Society.
# Color Visual Secret Sharing for QR Code with Perfect Module Reconstruction
## 3 The Proposed VSS-QR
### VSS-QR Encoder
For \(\textbf{H}(x,\ y)=1\), we may get \(\textbf{S}_{1}(x,\ y)=\texttt{B}\), \(\textbf{S}_{2}(x,\ y)=\texttt{R}\), \(\textbf{S}_{3}(x,\ y)=\texttt{G}\) and \(\cap_{i=1}^{3}\textbf{S}_{i}(x,\ y)=\texttt{W}\). By applying the above encoding algorithm to each module of **H** sequentially, we get three shares, each having \(D\times D\) modules, where \(D\) is the number of modules along each row/column of the secret QR code. For QR code with version \(V\), \(D\) can be calculated as:  
\[D=17+4\times V. \tag{12}\]  
To render the shares as images, we must determine the size of each module. Let the size of each square module be a \(L\times L\) pixels, then each share image has \(L\cdot D\times L\cdot D\) pixels. For convenience, we use the same letter for QR code image with module indexing and QR code image with pixel indexing, where brackets are used for pixel indexing and parentheses are used for module indexing. For example, the QR code image **H** is written as \(\textbf{H}(x,\ y)\) when accessing the \((x,\ y)\) module, but is written as \(\textbf{H}[i,\ j]\) when accessing the \([i,\ j]\) pixel, where \(1\leq x,y\leq D\) and \(1\leq i,j\leq LD\). The image rendering process can be expressed as:  
\[\textbf{H}[i,\ j]=\textbf{H}(x,\ y), \tag{13}\]  
for \(i\in[(x-1)L+1,\ xL]\), \(j\in[(y-1)L+1,\ yL]\). The above encoding algorithm is summarized in Algorithm 1. ```
0:
1:Scert QR code H
2:Three shares: \(\textbf{S}_{1},\textbf{S}_{2},\textbf{S}_{3}\). 3:
4:Determine the two sets \(\mathcal{E}_{0}\) and \(\mathcal{E}_{1}\): \(\mathcal{E}_{0}\sim\left\{\left\{\textbf{G},\textbf{B}\right\},\left\{\textbf{R},\textbf{B}\right\},\left\{\textbf{R},\textbf{G}\right\}\right\}\), \(\mathcal{E}_{1}=\left\{\textbf{R},\textbf{G},\textbf{B}\right\}\)
5:for\(x\) from \(1\) to \(D\)do
6:for\(y\) from \(1\) to \(D\)do
7:if\(\textbf{H}(x,\ y)=0\)then
8:\(\textbf{S}_{1}(x,\ y)\sim\mathcal{E}_{0}\), \(\textbf{S}_{2}(x,\ y)\sim\mathcal{E}_{0}\), \(\textbf{S}_{3}(x,\ y)\sim\mathcal{E}_{0}\backslash\left\{\textbf{S}_{1}(x,\ y)\right\}\). 9:else
10:\(\textbf{S}_{1}(x,\ y)\sim\mathcal{E}_{1}\), \(\textbf{S}_{2}(x,\ y)\sim\mathcal{E}_{1}\backslash\left\{\textbf{S}_{1}(x,\ y) \right\},\) \(\textbf{S}_{3}(x,\ y)\sim\mathcal{E}_{1}\backslash\left\{\textbf{S}_{1}(x,\ y), \textbf{S}_{2}(x,\ y)\right\}.\)
11:endif
12:\(\textbf{S}_{\ell}[i,\ j]=\textbf{H}(x,\ y)\), for \(i\in\left[(x-1)L+1,\ xL\right],j\in\left[(y-1)L+1,\ yL\right],\ell=1,\ 2,\ 3\). 13:endfor
14:endfor
```  
**Algorithm 1** (3,3)-threshold VSS-QR algorithm
## 3 Results
### Comparative Analysis of HT-qPCR with Shotgun Metagenomic Sequencing
#### 3.2.3 Drug Classes  
The gene classes recorded with the SMS method were more in numbers (28) and diversity. In agreement with the HT-qPCR method, beta-lactams (cephalosporins. Cephamycin, carbapenem, penam, penem, and monobactam) dominated the gene classes (37%). This was followed by macrolides (19%) and tetracycline (7%). The remaining gene classes were fluoroquinolone (6%), Incosamide (4%), phenicol (4%), streptogramin (4%), peptide (3%), glycoptide (3%), aminocoumarin (3%), diaminopyrimidine (trimethoprim) (2%), rifamycin (2%), glycylcycline (1%), acridine dye (1%), triclosan (1%), pleuromutilin (1%), sulfonamides (1%), and fosfomycin (1%) (Figure 5a). In addition to these, low-abundance genes (<1.0%) such as Nucleoside, Oxazolidinone, Fusidic acid, Muiprocin, Efamycin, Nitroimidazole, Antibacterial free fatty acids, and Nitrofuran were also picked with the SMS method (Figure 5b).  
The HT-qPCR is limited in capturing the low copy numbers of genes (<10). This is most likely the reason for null amplification at sites 6, 9, 10, 11, and 12 (Figure 5c). With the SMS method, genes belonging to these drug classes were observed at all the sites (Figure 5d),  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline
**Gene** & **Site 1** & **Site 2** & **Site 3** & **Site 4** & **Site 5** & **Site 6** & **Site 7** & **Site 8** & **Site 9** & **Site 10** & **Site 11** & **Site 12** \\ \hline Bacteroidetes & 0.23 & 0.44 & 0.19 & 0.37 & - & 1.27 & 0.13 & 0.84 & - & - & - & 1.99 \\ Firmicutes & 0.14 & 0.08 & 0.27 & 0.11 & 0.14 & 0.55 & 0.23 & 0.13 & - & - & - & 0.29 \\ _Acinetobacter humammii_ & - & - & - & - & - & - & - & - & - & - & - \\ _Campylobacter_ & - & - & - & - & - & - & - & - & - & - & - & - \\ _Enterococci_ & - & - & - & - & - & - & - & - & - & - & - & - \\ _Klebsiella, pneumomidae_ & - & - & - & - & - & - & - & - & - & - & - & - \\ _Pseudomonas aeruginosa_ & - & - & - & - & - & - & - & - & - & - & - & - \\ _Staphylococci_ & - & - & - & - & - & - & - & - & - & - & - & - & - \\
16S rRNA & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\ \hline \end{tabular}
\end{table}
Table 2: Taxonomic profiles of bacterial domains observed through HT-qPCR.  
suggesting the ability of SMS to capture even the low-abundance gene, where less than ten copies of genes were present. As evident from Figure 5d, many genes exhibited resistance against multiple drugs. This was missed with the HT-qPCR assay.  
Figure 3: Box plot showing the distribution of resistances in marine surface sediments. The blue boxes represent the HT-qPCR method, and the red boxes represent the SMS approach. The black square shows the average values of each gene type. The whiskers represent the standard deviation, whereas the blue balls are the outliers.  
Figure 4: Core resistance identified with the SMS method.  
Figure 5: Drug classes identified with high-throughput molecular methods (a) HT-qPCR and (b) SMS. (c) Relative abundances of drug classes identified with HT-qPCR. (d) Relative abundances of drug classes identified with SMS.
## 4 Construction of a Control System Model and Test Results
### Simulation Tests of a Control System with an Inertial Block (\(F_{c1}\)) of the Actuation System
Simulation Tests of the Control System with an Inertial Block (\(F_{c1}\)) of the Actuator System and a Disturbance Generator of the Measurement Signal  
The control system with an inertial block (\(F_{c1}\)) of the actuation system and a disturbance generator of the measurement signal was connected according to the diagram shown in Figure 10 in order to perform tests. The last set of tests was performed similarly to Section 4.2, except that a disturbance was added to the measurement signal. The disturbance was generated using MatLab Simulink's Random Number block. In the Random Number block, the 'variance' parameter was set to 0.003, and the time for the generation of disturbing samples was set to 0.01 s. An example of a measurement signal interference is shown in Figure 11. All control system parameters were set as in the control system with an inertial block (\(F_{c1}\)) of the actuator system, as shown in Section 4.2. Three tests were performed for the TR controller. In each test, the \(k_{c}\) parameter was set to a value of 500. In the tests, the effect of measurement signal disturbances on the quality of the control was shown. As before, a constant value of \(\varphi_{min}=0.05\) was set during the tests, while the initial \(\varphi_{0}\) value was changed, taking values of \(\varphi_{0}=1\) (57.3\({}^{\circ}\)), \(\varphi_{0}=0.5\) (28.65\({}^{\circ}\)) and \(\varphi_{0}=0.2\) (11.46\({}^{\circ}\)) in subsequent tests. The test results for the TR controller are shown in Figures 12 and 13. Figure 11: An example of measurement signal interference. Figure 10: Diagram of the control system with disturbances and an inertial block. was adjusted from twice the starting value (\(\varphi_{0}\)). Figure 12 also shows that the disturbances extended the time to reach the set value of the TR controller itself, especially for the test at \(\varphi_{0}=1\) (57.3\({}^{\circ}\)). The adjustment time for this signal almost doubled.
## 4 Simulations and Analysis  
For intermittent arcs, the variation of the electric field within the cable insulation defect is the decisive factor in arc breakdown. For periodic current, the directional movement of ions in the liquid within the defect forms the fault current, and current distortion is directly related to ion migration. To further understand the phenomenon observed in the experiments, the electric field simulations and analysis of the ion migration are conducted.
## 1 Introduction
In [25; 26], researchers used the cross-relations in the multiple reception in order to complete channel estimation in single input multi output (SIMO) channel. In [25], a least square blind channel estimation method is proposed and the necessary condition for channel estimation based on cross-relations is analyzed in SIMO channel. In [26], an adaptive blind channel estimation algorithm is proposed. However, these blind channel estimation methods have huge amount of computation to do and do not consider the sparsity of underwater acoustic channel. In recent years, scholars have done lots of research on channel estimation methods based on sparse bayesian learning (SBL) and have achieved good result [27; 28; 29]. However, it is difficult to apply the SBL-based channel estimation method in the non-cooperative condition due to the lack of prior information. Moreover, the identification will fail because the original constellation cannot be achieved when the channel estimation result is applied to the likelihood method directly. In this paper, we consider likelihood-based identification of modulation types including BPSK, QPSK, and 8 PSK. Firstly, we use the cross-relations and SBL to estimate the CIR. Then, EM algorithm is used to further process the received signal after channel equalization. Finally, modulation can be identified successfully by likelihood-based method. The proposed method can effectively improve the identification rate in the underwater acoustic multipath channel. The proposed SBL-EM-QHLRT method provide the following contributions in this regard:  
* Aiming at the problem that the traditional likelihood-based modulation identification method cannot achieve the identification in underwater acoustic multipath channel, we propose a blind channel estimation method based on SBL, which can eliminate the impact of multipath on the identification effectively. * Because of the error of SBL blind channel estimation and the unknown original constellation mapping of MPSK, we model the signal processed by SBL as Gaussian mixture model (GMM), and use EM in corr to correct the constellation and compensate the error of channel estimation. The rest of the paper is organized, as follows. Section 2 shows a SIMO underwater acoustic system model, and the cross-relation in SIMO channel is derived. A Novel method named SBL-EM-QHLRT is developed in Section 3. Section 4 discusses the performance of SBL-EM-QHLRT method through numerical simulation. Conclusions are drawn in Section 5.
# An Improved YOLOX Model and Domain Transfer Strategy for Nighttime Pedestrian and Vehicle Detection
## 3 Improved YOLOX Detection Algorithm
### Loss Function Design  
#### 3.5.1 Confidence Loss Function Design  
In the process of nighttime vehicle and pedestrian detection based on the YOLOX model, due to the anchor-free mechanism and the large ratio of foreground-to-background pixels in the experimental data, the positive and negative samples in the prediction stage of the algorithm are in an extremely unbalanced state, which makes it difficult for the model to be fully trained. In addition, the target locations of the scenes in the experimental dataset are more concentrated and most of them are in occlusion, which easily leads to target miss detection.  
To address the above two problems, Varifocal [66] was introduced as the confidence prediction loss function. Varifocal is mainly used to train the dense target detector to perform the Intersection Over Union (IoU)-aware classification score regression to improve the detection accuracy. Target occlusion is one of the features of dense targets, so using this loss function can alleviate the missed detection phenomenon caused by target occlusion. The Varifocal loss function is also based on the weighting method of Focal Loss [24], which can improve the positive and negative sample imbalance during the training process. The formula of the Varifocal loss function is shown in Equation (2).  
\[L_{\text{vf}}(p,q)=\begin{cases}-q(q\log(p)+(1-q)\log(1-p))&q>0\\ -ap^{\gamma}\log(1-p)&q=0\end{cases} \tag{2}\]  
where \(p\) is the prediction confidence after the Sigmoid activation function; \(q\) is the IoU value of positive samples with Ground Truth; \(a\) is used to reconcile the balance between positive and negative samples, which is 0.25; and \(\gamma\) is used to reconcile the balance between easy and difficult samples, which is 1.5.