esa-sceva/satcom-chunk-collection huggingface dataset:
# Learning Attention-Aware Interactive Features for Fine-Grained Vegetable and Fruit Classification
## 1 Introduction
A similar study named Convolutional Block Attention Module (CBAM) [18] also explores both channel and attentive spatial features that allow a model to learn what and where to focus. Inspired by prior efforts, we propose a framework named Attention-aware Interactive Features Network (AIF-Net) that enhances fine-grained feature learning through an integration of a biattention (spatialwise and channelwise attention) module [18] and a modified Attentive Pairwise Interaction module [14] into a backbone network. Specifically, the proposed AIF-Net consists of three components, including: (1) An attentive feature extractor that allows the network to identify and learn from critical areas in an image where distinguishable patterns may reside in. In addition, to exploit both global and local feature maps, we integrate a region proposal network (RPN) into the pipeline to generate a collection of informative regions; the top regions are selected to create attentive local features that are fused with the attentive global feature. The fusion output is utilized by the downstream components of the network to enhance mutual and individual feature learning. (2) An interactive feature learning module that learns to distinguish subtle pattern differences in an image pair through pairwise feature interaction, and (3) a softmax classifier with individual and pair regularization terms that can effectively utilize the attentive features to optimize the underlying neural network. We conduct extensive experiments to evaluate the performance of the proposed AIF-Net on the VegFru data set. Two key design choices, including the fusion operation (concatenation vs. summation) and the number of top-scoring informative regions used for local feature extraction, are validated. We also report an overall performance comparison between AIF-Net and its peers, including ResNet [21], VGG [22], Compact Bilinear Pooling (CBP) [9], HybridNet [13], Destruction and Construction Learning (DCL) [23], Weakly Supervised Data Augmentation Network (19) (WS-DAN) [24], and API-Net [14], and the latter four represent the SOTA. Results demonstrate a consistent performance improvement of our AIF-Net over other comparative models, validating the effectiveness of the proposed neural architecture in FGVC. The rest of this paper is structured as follows. In Section 2, we review the related studies on FGVC. In Section 3, we provide the technical details of the proposed AIF-Net. In Section 4, we report the experimental results with analysis and insights. In Section 5, we provide a discussion and point out potential extensions.
## 3 Results
### Static Accuracy
(MF: multi-frequency; \(\bar{x}\): arithmetic mean; SD: standard deviation; \(CEP_{xx}\): circular error probable of xx%; CEP: circular error probable of 50%; DRMS: distance root mean square; 2 DRMS: twice distance root mean square, n: number of measurements). differences (not taking into account the poorly performing Samsung Xcover 4 smartphone) are tiny, and the single-frequency smartphones reach a DRMS value of 7.23 m.  
Figure 7: Carrier to noise density ratio C/N\({}_{0}\) skylplots of the GNSS signals collected by the different smartphones. If another index is used, such as the CEP (median), this ratio is put into perspective again, whereby the error difference of multi-frequency smartphones, 4.73 m, is significantly lower than that of the classic single-frequency smartphones, 6.02 m. (see Figures 8 and 10 and Table 4). Figure 8: Relative cumulative GNSS accuracy distribution of different smartphones under different forest canopy conditions. Blue indicates smartphones that have multi-frequency capabilities, and red indicates smartphones that can only receive the L1/G1/E1/B1l bands. Figure 9: Correlation between the absolute position error (CEP) and (**a**) the release date and (**b**) the mean number of active satellites used by the smartphones used in the experiment. A correlation between the absolute position error (CEP) and the number of active satellites is not clear (Figure 9b). The Huawei P20 and the Huawei P40, in particular, clearly demonstrate this, as the P40 uses about three times the number of satellites to determine its position while having similar positioning accuracy. Therefore, other factors greatly influence the precision of the positioning. However, the most accurate smartphone (Xiaomi Mi 10 light) also has a very high number (25.8) of active satellites. Table 5 shows a Wilcoxon Rank Sum Test to compare the different receivers relating to the absolute position errors. All the accuracy differences between the smartphones are significant, except for the Huawei P40 and the Xiaomi Mi 8 Pro. The identical (Samsung Xcover 4s) or quasi-identical (Xiaomi Mi 8/8 Pro) ones, on the other hand, are significantly different.
# Fake Base Station Detection and Link Routing Defense +
## 8 Future Directions Discussion
### Advancing Detection  
Our work focuses on threat detection and blacklisting, focusing on fake base stations with malicious intent. Our work can extend to a multiclass detection scheme to have distinct layers and levels for different classes, including the anomaly of faulty. For example, instead of the binary classification of fake vs. legitimate, we can combine anomaly and threat detection and introduce three classes of fake vs. faulty vs. legitimate.  
For richer detection, we can combine our information features for detection and blacklisting with those from the previous research described in Section 2.2. However, wefocus on our research/novel contributions (the observations of the time and number of request transmissions, which have not been analyzed in the previous research in fake base station detection) and their performances and analyses in this paper.  
Another future direction will be to design and implement dynamic and adaptive schemes. Such dynamic control and adaptability can be applied to detection, including controlling and varying the threshold values.  
Machine learning, for example, reinforcement learning based on a connectivity service reward or anomaly detection based on the deviations from the set protocol, can be used as an alternative mechanism for detection.
# Multi-Identity Recognition of Darknet Vendors Based on Metric Learning
## 2 Related Works
### Features of Classification  
Some researchers have used writing-style features to identify vendors' multiple identities on the dark web. [PERSON] et al. [16] conducted a writing-style feature analysis of dark web market data from eight years (2011-2018) and identified 22,163 vendor accounts with sales records controlled by 15,652 different real-world vendors. They used a wide range of features, including edit distance between the IDs, same or different marketplace, Jaccard similarity for profile descriptions, and item titles and descriptions.  
In addition to writing-style features, many other types of information can be obtained from dark web vendors. [PERSON] et al. [17] proposed the eDarkFind model, which summarized five types of features to describe a vendor, including the description text of their products, the location of the product, and the product category. They proposed an unsupervised cosine similarity matching method to detect a vendor's multiple identities on dark web marketplaces, using the ability of multi-view learning to capture different and complementary information in the features. The accuracy of predicting vendor similarity reached 98%. [PERSON] et al. [18] were the first to construct an information network of dark web vendors based on PGP key relationships and apply writing-style features to dark web marketplaces. The analysis found that many vendors used different usernames on different markets but the same PGP key as their identity.  
Some studies have used images published by vendors as features, based on the assumption that a vendor is likely to use the same image to describe similar products on different trading markets, or may take pictures of the same product from multiple angles. [PERSON] et al. [19] extracted features from images published by vendors and studied them on three dark web markets (Agora, Evolution, and Silk Road 2). They used a special dataset labeling method, dividing each image published by a vendor into two parts, which were considered different identities of the same vendor. The classifier's task was to classify these two parts into the correct vendor. Then, multiple pretraining models were used to construct a multiclass classifier, with each category representing a real-world vendor. Finally, the model achieved an accuracy ranging from 58% to 84.6% on the three dark web market datasets, after removing duplicate images. However, this labeling method prevented the model from conducting a cross-market analysis, while we prefer to identify accounts controlled by the same vendor in different markets.
# Upgrades of a Small Electrostatic Dust Accelerator at the University of Stuttgart
## 2 Materials and Methods  
A compact electrostatic dust accelerator mainly consists of one high voltage power supply, one dust source, one focusing lens, dust detectors, target chambers and a high vacuum system. Our previous small accelerator is shown in Figure 1. A schematic diagram of our upgraded small dust accelerator with the PSU with 3 additional dust detectors and deflection stage is given in Figure 2. The test setup of the linac stage consists of one dust source, one focussing system, one differential dust detector (2 CSA channels), the linac stage and one single end dust detector after the linac (see Figure 3).  
The dust source is the heart of an electrostatic dust accelerator first developed by reference [4]. The dust source in small accelerators serves two purposes: charging dust particles and accelerating them to final speeds. Dust particles are filled into the reservoir during assembly. A pulser unit connected to the reservoir applies a time-varying electric field between the needle and the reservoir. Dust particles are charged, lifted and pushed through a small hole out of the reservoir. A grounded extraction plate further accelerates charged particles to their final speeds. A beam collimator system with grounded metal rings with a diameter of 0.8 mm leads particles into the accelerator beam line. The entire accelerator employs a high-vacuum system to avoid sparking caused by the high electric field strength in the dust source.  
A comparison of our previous small accelerator and the upgraded setup is shown in Table 1 and will be described in detail in the following sections.  
Figure 1: Small dust accelerator with short beam line. The high voltage generator (Brandenburg 2807 Alpha Serial II) produces an acceleration voltage up to 30 kV. The dust source is operated in air condition and no protection gas (such as SF6) is required. The beam line itself is evacuated to a vacuum of better than \(10^{-6}\) mbar using a turbo molecular pump.  
Figure 3: Schematic diagram of the entire accelerator with post-stage linac.  
Figure 2: Schematic of the entire system of the upgraded small dust accelerator.  
The velocity obtained by a particle in an electrostatic field depends on its charge-to-mass ratio and acceleration voltage. If a particle of mass \(m\) and surface charge \(q\) is accelerated through potential \(U_{acc}\), the speed \(v\) is obtained from the energy law by:  
\[v=\sqrt{2\cdot\frac{q}{m}\cdot U_{acc}} \tag{1}\]
# Fault-Tolerant Control for Carrier-Based Aircraft Based on Adaptive Fuzzy Sliding-Mode Method
## 5 Simulation Results
### Simulation 2  
Next, the superiority of the designed AFSMFTC method over the conventional PID method in tracking and anti-disturbance performance in a normal landing is verified via the simulation results shown in Figures 7-9.  
\begin{table}
\begin{tabular}{c c} \hline
**Sliding Mode Gain Parameter** & **Reference Value Range** \\ \hline \(c_{\phi}\) & [5; 10] \\ \(c_{\theta}\) & [5; 11] \\ \(c_{\beta}\) & [7; 13] \\ \(c_{p}\) & [8; 17] \\ \(c_{q}\) & [6; 12] \\ \(c_{r}\) & [8; 15] \\ \hline \end{tabular}
\end{table}
Table 1: Selection of the sliding mode gain range.  
Figure 6: Response curves of pitch angle: (**a**) Response curves of pitch angle under the step signal; (**b**) Response curves of pitch angle under the sinusoidal signal.  
Furthermore, the impact of the carrier air wake in the longitudinal direction on the landing performance of the CBA is far higher than that in the lateral direction during a normal landing, as shown in Figure 9. Therefore, the AFSMFTC-based ACLS method is distinctly superior to the conventional PID method in controlling the angle of attack, pitch angle and airspeed in the longitudinal direction, and it is more capable of keeping the angle of attack, velocity and pitch angle in smaller fluctuation ranges. However, the two methods differ less in the lateral direction.
## 4 Materials and Methods  
### Plant Materials  
Four _Actinidia_ species, namely, _A. latifolia_, _A. rufa_, _A. zhejiangensis_, and _A. cylindrica_, were selected for AsA analysis in this study. The plant materials were cultivated in the Wuhan Botanical Garden, Chinese Academy of Science (N30\({}^{\circ}\)32\({}^{\prime}\), E114\({}^{\circ}\)24\({}^{\prime}\)). Plant fruits at different developmental stages were collected from each of these four kiwifruit species, including collections at 20 days after fruiting (DAF20), DAF40, DAF60, DAF80, DAF100, DAF120, DAF140, and DAF160. Meanwhile, calli of _Actinidia_ eriantha_ were grown at 25 \({}^{\circ}\)C under 16 h light/8 h dark conditions and were subcultured into new MS agar media every 30 days. Only transgene-positive calli were collected. All plant samples were immediately frozen in liquid nitrogen and stored at \(-\)80 \({}^{\circ}\)C.
# Image Deraining Algorithm Based on Multi-Scale Features
## 2 Related Work
### Model-Based Rain Removal Method  
The model-driven rain-removal-based approach uses advanced algorithmic techniques and physical models to process and remove raindrops from images. The core of this approach lies in the construction of a model that can simulate the process of rain formation in an image and through which a clean background of the image can be separated and recovered. Traditional model-based methods still have far-reaching significance and huge reference value for current rain removal research. Therefore, the current research status of traditional model-based image rain removal algorithms will be introduced below.  
As early as 2004, [PERSON] and [PERSON] [3; 4] began research on rain removal from video images. [PERSON] et al. [5], as the leader in the field of single-image rain removal, extracted the high-frequency layer of the rain image in 2012 and used dictionary learning and sparse coding technology to further decompose it into rain components and non-rain components. In 2015, [PERSON] et al. [6] proposed a learning dictionary that learns both the rain layer and the background layer. In 2016, [PERSON] et al. [18] proposed a method based on the Gaussian mixture model applied to both the background layer and the rain pattern layer to adapt to various background appearances and the appearance of rain patterns. In 2017, [PERSON] et al. [19] used joint optimization to alternately remove rain details from the background layer and remove non-rain details from the rain layer.  
The model-based single-image rain removal method uses a large amount of physical prior knowledge as the basis, and physical modeling requires a large amount of comprehensive knowledge; there are many factors to consider, so it is difficult to cover everything. Its rain-removal effect has been proven through practice. It is highly dependent on the reliability and accuracy of the established model and cannot be widely used in the real world. The generalization performance still needs to be improved. Therefore, it is difficult to use traditional physical models to remove rain. However, rain removal methods based on prior knowledge still have a profound impact on current research and provide inspiration for current rain removal methods [20].
## 4 Spatio-Temporal Big Data Visualization Framework
### The Generation of the MAP Pyramid  
During the construction of the MAP pyramid, the input raw dataset is processed and hierarchically aggregated from bottom to top. First, the bottom layer is calculated, after which the upper layers are iteratively derived layer by layer. The pseudocode of the hierarchical aggregation process is presented in Algorithm 1; moreover, the complete preprocessing steps are explained as follows:  
Step 1: Define the required input parameters, including the maximum pyramid level \(l_{n}\) and the time granularity \(t_{b}\). Via the _Extract_ operation defined in Section 3.2, the spatial and temporal coordinates are extracted from the original records along with the _fa_tree_. As a result, the discrete _st-pixels_ are obtained.  
Step 2: With help from Spark's Map/Reduce function also, the _st-pixel_ in the same _st-tile_ in the same time interval \(t_{k}\) and the same _fa_tree_ cell \(a_{j}\) are grouped into one _st-tile_ using the _Group_ operation.  
Step 3: The _st-tile_ level \(l\), the space-filling curve code of the tile \(k\), the time \(t\), and the _fa_tree_ cell are joined to generate a _Key_, after which the _st-tile_ is assigned as a _Value_ to form a key-value pair with the _KVP_ operation introduced in Section 3.2. Finally, the bottom level \(l_{n}\) in the MAP pyramid is obtained.  
Step 4: After Step 3 is complete, the iterative aggregation can be conducted continuously to obtain the \(l_{n-1}\), \(l_{n-2}\),..., 1 layer by layer and construct a multi-dimensional aggregation pyramid using the _Aggregate_ operation.  
Since all the steps are pure loops, they can be implemented using the Spark's Map/Reduce function and executed in a distributed Linux cluster.  
Figure 8: The system architecture of MAP-Vis.  
```
1:Original record, Rm(\(lng\), \(lat\), \(time\), \(attributes\)), m = 1, 2,..., N;
2:The maximum map level, \(l_{n}\);
3:The time granularity;
4:\(l_{n}\) (i = 0, 1, 2,... n), hierarchical aggregation result;
5:for each record \(R_{i}\) in original records do
6:\(f\)a_\(tree\) = flatten(\(attributes\))
7:\(t_{j}\) = iBin(\(time\))
8:\(ST\_{i}\)\(l_{ij}\) = Extract(\(lng\), \(lat\), \(ln\), \(f\)a_\(tree\), \(t_{j}\))
9:endfor
10:for each \(Pixel_{ij}\) in the same Tile with \(Tile_{ij}\)do
11:Group Pixel\({}_{ij}\) to \(Tile_{ij}\)
12:endfor
13:for each \(Tile_{ij}\) with \(F\)a_\(tree\) = A\({}_{\text{all}}\), A\({}_{1}\),..., As do
14:Key-Value \(Tile_{ij}\) to \(Tile_{ij}\)
15:endfor
16:\(Tile_{ln}\) \(\leftarrow\)All(\(Tile_{ij}\))
17:for\(i\) = \(ln_{-1}\), \(ln_{-2}\),..., 1 do
18:\(Tile_{ii}\) = Aggregate(\(Tile_{i+1}\))
19:endfor
20:return\(Tile_{ii}\), \(i\) = 0, 1, 2,... n;
```  
**Algorithm 1** The pseudocode of the MAP preprocessing algorithm
# A Sparsity-Invariant Model via Unifying Depth Prediction and Completion
## 5 Experiment
### Experimental Results in Real-World Application
#### 5.5.1 Testset  
Speckle-type sparse ToF depth sensors can reduce the power consumption of the entire module by reducing the number of speckles, making them widely used in lightweight terminals such as smartphones. Speckle-type ToF cameras encounter common issues inherent in ToF cameras, including relatively low depth map resolution, black holes in the depth map due to objects beyond the hardware measurement range, occlusion and noise around object boundaries, as well as susceptibility to external environmental interference. Additionally, they possess unique characteristics: the depth maps obtained by speckle-type ToF cameras usually exhibit extremely high sparsity (typically less than 2% effective depth values). In this experiment, we used the OPPO Spot-iToF316 as the hardware device for acquiring sparse depth maps. We captured 180 sets of color images and corresponding sparse depth maps in different indoor scenes to create the test dataset. The resolution of both the color images and sparse depth maps in the test dataset is 256 \(\times\) 192.
applied sciences  
Solid Lipid Nanoparticles as Carriers for the Synthetic Opioid LP2: Characterization and In Vitro Release  
[PERSON] 1,*, [PERSON] 1(c), [PERSON] 1, [PERSON] 2, [PERSON] 2(c), [PERSON] 1, [PERSON] 3 and [PERSON] 2,*, 2  
Department of Drug and Health Sciences, Medicinal Chemistry Section, University of Catania,  
Viale Andrea Doria 6, 95125 Catania, Italy; [EMAIL_ADDRESS] (L.P.); [EMAIL_ADDRESS] (M.L.); [EMAIL_ADDRESS] (R.T.)  
Department of Drug and Health Sciences, Pharmaceutical Technology Section, University of Catania,  
Viale [PERSON] 6, 95125 Catania, Italy; [EMAIL_ADDRESS] (L.M.S.); [EMAIL_ADDRESS] (M.G.S.)  
Department of Drug and Health Sciences, Pharmacology Section, University of Catania, Viale [PERSON] 6, 95125 Catania, Italy; [EMAIL_ADDRESS]  
Correspondence: [EMAIL_ADDRESS] (A.S.); [EMAIL_ADDRESS] (L.M.);  
Tel.: +39-095-738-4002 (A.S.); +39-095-738-4010 (L.M.)  
**Featureud Application: The incorporation of the synthetic anti-nociceptive ligand LP2 into solid lipid nanoparticles would allow its parenteral administration, thus improving its efficacy. As LP2 is a diastereoisomeric mixture of 2R/2S-LP2, the HPLC method, developed and validated in this work to separate and quantify each diastereoisomer, could be useful in further pharmacological studies.**  
A synthetic dual-target mu opioid peptide receptor/delta opioid peptide receptor anti-nociceptive ligand, named LP2, has emerged as a promising candidate for the management of acute and/or persistent pain, but its lipophilicity limits further developments as a therapeutic agent. In this work, to allow designing aqueous formulations of LP2 for parenteral administration, solid lipid nanoparticles (SLNs) were investigated as LP2 nanocarriers. LP2-loaded SLNs were prepared by the phase-inversion temperature method, showing good technological properties (small mean particle, size, low polydispersity index, good stability). As LP2 was a diastereoisomeric mixture of 2R/2S-LP2, an HPLC method was developed to identify and quantify each diastereoisomer, and this method was used to assess LP2 in vitro release from SLNs. The developed method, based on reverse-phase chromatography using an isocratic mobile phase consisting of 50% methanol and 50% triethanolamine at 0.3% (pH = 3 with trifluoracetic acid), allowed efficient separation of 2R- and 2S-LP2 peaks and reliable quantification with intra- and inter-day precision and accuracy within the acceptability limit, expressed as relative standard deviation set \(\leq\)15%. The results of this study suggest that the incorporation of LP2 into SLNs could be a promising strategy to design suitable formulations for further pharmacological studies involving LP2.  
**Keywords:** solid lipid nanoparticles; diastereoisomers; LP2; multitarget ligand; opioids; anti-nociceptive agents; HPLC