esa-sceva/satcom-chunk-collection huggingface dataset:
# Research and Design of a Decentralized Edge-Computing-Assisted LoRa Gateway
## 2 Related Work
### LoRa Gateway and System  
As described in Section 1, LoRa communication exhibits distinctive characteristics, such as simple and cost-effective networking, robust interference resistance, and unparalleled advantages in large-scale low-power networking and IoT services in remote areas, where cellular base stations are inaccessible. The fundamental structure of a LoRa communication system is illustrated in Figure 1, where end devices transmit data packets to gateways, which in turn route them to network servers via either cellular networks or Ethernet. The focus of this paper is investigating how to leverage distributed computing to maximize the gateway capabilities at the edge for data processing, and thereby alleviate the burden on cloud computing centers. The hardware design of both the terminal devices and the LoRa gateway can be customized to accommodate different types of sensor interfaces and service requirements.  
At the LoRa communication level, several implementations of LoRa systems were proposed in [8], including XisLoRa, Chirp Stack, and the Things Network (TTN). The communication architecture design proposed in [8], which was targeted at different applications for uplink and downlink, has provided inspiration for the design approach in this article.  
In the field of distributed computing, [PERSON] et al. [9] and [PERSON] et al. [10] both proposed applying the technology of the blockchain to LoRa networks. However, their proposed approach was to deploy blockchain technology in the cloud, which would not be effective in reducing the load pressure on the cloud, but would rather increase the processing workload of the cloud. The LoRa gateway's edge computing capabilities would not be activated. [PERSON] et al. [11] proposed a LoRa system based on edge computing which migrates some of the access control command instructions from the cloud server to the LoRa gateway, thus effectively relieving the burden of cloud processing. [PERSON] and [PERSON] [12] proposed a LoRa system based on blockchain technology. This system would give different devices with different computing and storage capacities different ways to manage their data. High-capability LoRa gateways can download the whole blockchain, but low-capability LoRa gateways can only obtain block headers.  
Regarding the distributed applications of LoRa, most of the existing literature (e.g., [13; 14]) focuses on utilizing deep learning techniques for processing the collected data on LoRa gateways and end devices, with applications in areas such as fall detection and agriculture. However, there is relatively little research on the massive uplink end device access and  
Figure 1: LoRa gateway system.  
multi-task downlink publication to end devices using LoRa. LoRa gateways typically only provide data relay services between end devices and cloud services, with limited utilization of resources beyond their communication functions. To use edge computing in LoRa gateways, the system architecture of LoRa gateways must be refactored, and the computing resources must be evaluated.
_Article_  
Electro-Optical Ion Trap for Experiments with Atom-Ion Quantum Hybrid Systems  
[PERSON]\({}^{1,\star}\)\({}^{\copyright}\), [PERSON] \({}^{1}\)\({}^{\copyright}\) and [PERSON] \({}^{1,2,3}\)\({}^{\copyright}\)  
Received: 28 February 2020; Accepted: 20 March 2020; Published: 25 March 2020  
**Featured Application: This work reports the novel design of a micromotion-free electro-optical trap for ions, integrated with a standard linear Paul trap. The ion trap is developed for experiments with atom-ion quantum mixtures and allows the ions to reach ultracold temperatures by sympathetic cooling with the neutral atoms, eventually leading to atom-ion collisions in the s-wave scattering regime.**  
**Abstract:** In the development of atomic, molecular, and optical (AMO) physics, atom-ion hybrid systems are characterized by the presence of a new tool in the experimental AMO toolbox: atom-ion interactions. One of the main limitations in state-of-the-art atom-ion experiments is represented by the micromotion component of the ions' dynamics in a Paul trap, as the presence of micromotion in atom-ion collisions results in a heating mechanism that prevents atom-ion mixtures from undergoing a coherent evolution. Here, we report the design and the simulation of a novel ion trapping setup especially conceived of for integration with an ultracold atoms experiment. The ion confinement is realized by using an electro-optical trap based on the combination of an optical and an electrostatic field, so that no micromotion component will be present in the ions' dynamics. The confining optical field is generated by a deep optical lattice created at the crossing of a bow-tie cavity, while a static electric quadrupole ensures the ions' confinement in the plane orthogonal to the optical lattice. The setup is also equipped with a Paul trap for cooling the ions produced by photoionization of a hot atomic beam, and the design of the two ion traps facilitates the swapping of the ions from the Paul trap to the electro-optical trap.  
**Keywords:** Paul traps; ion optical trapping; atom-ion interactions; atom-ion mixtures
# Security Enhancement for Data Migration in the Cloud
## 1 Introduction
### Cloud Data Storage versus Traditional Data Storage
#### 1.1.3 Archival and Disaster Recovery Purposes  
Cloud storage provider can help the organization enhance security from Internet services, by preventing loss due to fire, theft, or disaster. Therefore, companies who archive their information in the cloud do not have to worry too much about natural disasters.
## 3 Results  
The PCB and PAH contents of dried fruit were determined using four methods. Methods 1 and 2 (Figure 2) permitted determinations of PCBs (method 1) and PAHs (method 2) separately, while integrated methods 3 and 4 permitted determinations of PAHs and PCBs from single solvent extracts. Figure 2: Comparison of PCB contents in dried fruits analyzed with methods 1, 3, and 4. (**a**)—apricot, (**b**)—pear, (**c**)—apple; 1—determined with the specific method for PCBs; 3—determined according to the methods of [PERSON] and [PERSON] [32]; 4—determined according to the methods of [PERSON] et al. [33]. The values of PCB congeners in the material tested with methods 3 and 4 were significantly different from those obtained with method 1 (Figure 2, Table 4). With the standard method of PCB content determination, congeners 126, 169, and 156 were not detected in the fruits, and the highest content was of PCB 77 at 0.121 ng\(\cdot\)g\({}^{-1}\) dw (apricot). The amounts of PAH compounds determined with the standard method ranged from 0.05 ng\(\cdot\)g\({}^{-1}\) dw (benzo[g,h,i]terylene in apple) to 222.6 ng\(\cdot\)g\({}^{-1}\) dw (phenanthrene in pear). The analysis of PCB content using method 3 [32] did not determine the presence of congeners 105, 169, 81, 156, or 157. The highest value for PCB 28 at 0.150 ng\(\cdot\)g\({}^{-1}\) dw in pear. PAH content was determined with the same method and ranged from 0.17 ng\(\cdot\)g\({}^{-1}\) dw (benzo[ghi]perylene in apple) to 179.70 ng\(\cdot\)g\({}^{-1}\) dw/g dm (phenanthrene in pear). Method 4, developed by [PERSON] et al. [33], indicated that PCBs 105, 169, 81, 156, or 157 were not determined. The highest value determined was that of PCB 180 at 0.14 ng\(\cdot\)g\({}^{-1}\) dw (apple). Determinations of PAHs indicated that the lowest content was noted for dibenzo[a,h]anthracene at 0.24 ng\(\cdot\)g\({}^{-1}\) dw (apple), while the highest was for pyrene at 464.12 ng\(\cdot\)g\({}^{-1}\) dw (apple). A significant difference (\(p<0.05\)) was noted for PCB congener 180 in dried apple. Values obtained with methods 3 and 4 were 54 and 42 times higher, respectively, than the value obtained with method 1. Additionally, in contrast to method 1, some PCB congeners (in apple--PCBs 81, 105, 157; in apricot--PCB 105; in pear--PCB 165) were not determined with methods 3 or 4. However, PCB congeners 126 and 156 were determined in dried apple, but were not determined with method 1, which is used specifically for determinations of PCBs (<LOD). A very low, unacceptable recovery value was noted for PCB congener 169 (Table 4). In the current study, the model experiment conducted using standard solutions (Figure 3) indicated that methods 3 [32] and 4 [33] provided higher recovery values for all the PCB congeners analyzed, as well as for light PAHs, than did the specific methods for the two groups of these compounds. Particularly, large differences in PAH recoveries were
# Last-Mile Optimization Using Neural Networks
## 2 Related Work  
### Medical Domain Classification  
In their 2021 work, [PERSON] et al. [PERSON] et al. (2021) introduced an innovative ensemble classification technique, leveraging the Multilayer Perceptron (MLP) neural network for the purpose of diagnosing breast cancer. The emphasis on early detection in breast cancer is pivotal for ensuring prolonged survival. The outcomes of their predictions showcase promising results, achieving an accuracy rate of 98.74% when applied to the Breast Cancer Wisconsin dataset.  
In their 2019 study, [PERSON] and [PERSON] and [PERSON] (2019) aimed to introduce a machine-learning-based methodology for classifying electrocardiogram signals, which measure the heart's electrical activity, to effectively address irregularities in these signals. This is particularly crucial for detecting the patient's health status. The proposed approach utilized classification algorithms including Decision Trees (DT), Random Forests (RF), and Gradient-Boosted Trees (GBT). Initially, the signals were classified into two classes: normal and abnormal--and subsequently into four classes. The approach demonstrated notable performance, achieving an overall accuracy of 96.75% with the GBT algorithm and 97.98% with Random Forest for binary classification. In the context of multiclass classification, the approach attained an accuracy of 98.03% using the Random Forest algorithm.  
[PERSON] et al. (2022) [PERSON] et al. (2022) introduced a novel, multiclass classification approach for timely cardiovascular autonomic neuropathy (CAN) detection with an AUC score of 0.931.  
[PERSON] (2018) conducted a study to identify and classify cancer patients as high or low risk using various classifiers such as the Simple Logistic Regression (LR) method, MLP, RF, and DT. Simple LR yielded a maximum classification accuracy of 97.18%.  
In their 2022 study, [PERSON] et al. [PERSON] et al. (2022) introduced a data mining methodology called MLP-TLBO, which combines MLP with Teaching-Learning-Based Optimization (TLBO) for breast cancer diagnosis. The study compared the performance of this proposed method with other existing approaches across three datasets. The majority of the comparative analyses consistently showed the superiority of the proposed MLP-TLBO method, as evidenced by the obtained results.  
[PERSON] et al. (2018) [PERSON] et al. (2018) compared the performance of three ML algorithms such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and DT to detect which classifier works better in distinguishing malignant and benign tumors. The results of this study found SVM to offer the highest accuracy of 98.1% on the WBCD dataset.  
The medical industry now relies on technology to predict, analyze, and store medical information through images. The work of [PERSON] et al. (2019) [PERSON] et al. (2019) focused on the use of ML algorithms to classify liver patients using a liver patient dataset. The highest accuracy with nominal execution time taken was obtained by the RT algorithm.
## 7 Proteasome Inhibitors
### Epoxyketones  
Carfilzomib is an epoxyketone-based PI approved for the treatment of patients with relapsed and refractory MM [70; 71]. It is an analog of the natural compound epoxomicin and irreversibly blocks the \(\beta\)5 subunit of proteasome, resulting in the accumulation of ubiquitin-protein conjugates as well as the induction of programmed cell death. Past preclinical studies revealed that carfilzomib had less neurotoxicity than bortezomib and greater selectively for the CT-L subunit. At very high concentrations, it interacts with the \(\beta\)1 and \(\beta\)2 subunits [72]. Carfilzomib induces both external and internal apoptotic pathways as well as the activation of JNK and cytochrome c release.  
Figure 1: Structure of 26S proteasome and subunit specificities of PIs. (**A**) The 26S proteasome is composed of a core catalytic complex called 20S proteasome and two 19S regulatory complexes, which recognize the ubiquitinated proteins. The 20S region is a cylinder-shaped protease composed of different subunits organized in four rings in a \(\alpha\)/\(\beta\)7/\(\beta\)7\(\alpha\)7 configuration. The two outer \(\alpha\)-rings act as a link for the 19S complexes, whereas the two inner \(\beta\)-rings are responsible for proteolysis. (**B**) Cross-section of \(\beta\)-subunits. Three different catalytic activities, chymotrypsin-like (CT-L), trypsin-like (T-L), and caspase-like (C-L), are located in \(\beta\)5, \(\beta\)2, and \(\beta\)1 subunit, respectively. Bortezomib slowly reversibly inhibits the chymotrypsin-like activity (\(\beta\)5 subunit) and, at a higher concentration and in a much less potent manner, also targets the trypsin-like and caspase-like activities (\(\beta\)2 and \(\beta\)1 subunits, respectively). Carfilzomib irreversibly binds mainly the chymotrypsin-like activity, whereas izazomib reversibly inhibits the chymotrypsin-like activity. Both drugs at very high concentrations interact with the \(\beta\)2 and \(\beta\)1 subunits (**B**).
# Spatial Connections between Microplastics and Heavy Metal Pollution within Floodplain Soils
## 3 Results  
### Floodplain Soil Properties  
According to the FAO (2006) grain size classification, the studied floodplain soils of the Lahn River catchment consist mainly of floodplain silt foams with average composition of silt (48.03%) > sand (32.33%) > clay (19.64%). We found that the sand content decreases from the upper reaches (site ELM) to the lower reaches (site LIM), whereby it increases from an average depth of 100-150 cm to >60 cm in some cases, depending on the position within the floodplain (embankment or former flow channels) (Figures S1-S4). Maximum clay contents (>25%) are only reached within single clay layers (e.g., floodplain edge depression) (Figure 2c). The organic matter content ranges between 1.59 and 21.07% with a total average of 5.49%. Topsoils (0-30 cm) show significantly higher organic matter content (average: 7.35%) in contrast to subsoils (average: 4.66%). Absolute maximum values (>20.0%) occur within single organic clay-rich layers (clay mud). On average, the amount of organic material decreases with depth (in line with root density) (Figure 2a). The floodplain soils studied can, therefore, be classified as silty, organic-rich fluvial deposits, which is typical for fluvial deposits [40; 67]. Within the fluvial deposits, deposited during the Holocene, Fluivisols and Gleysols have developed [45]. From the preliminary study, it can also be deduced that for sediments close to the river (river bank and proximal floodplain), the upper 40-50 cm of the floodplain sediments are very young (deposition since the 1960s) [45]. The soil properties differ mainly in terms of land use, groundwater levels (hydromorphic conditions), and portions of coarse soil fragments (gravel). Horizon sequences contain Ah or Ap (Abp) above combinations of B, Bl, Bgl, Bgr, or Br horizons. Within the Fluivisols and Gleysols, the soil pH ranges between 5.11 (moderately acid) and 6.94 (neutral) with a total average of 6.12 (very weak acid) (Figure 2b). Regarding the dependence of heavy metal mobilization on the pH value, only the mobilization limit value of Cd (at 6.5) falls below this level (Figure 2b [2]).  
In summary, floodplain soil properties have the potential for an accumulation of heavy metals and (micro-)plastics. Whereas, on the one hand, the comparatively small amounts of clay and fine silt indicate reduced heavy metal adsorption (clay minerals), the proportionof organic matter and the only slightly acidic pH value show tendencies for low heavy metal mobility and adsorption of organic constituents [4].  
Figure 2: Floodplain soil properties for sampling areas. (**a**): Organic matter content (%) according to LOI analyses (maximum outliers excluded); (**b**): pH (-) values with mobilization limit values (1: Cd; 2: Zn, Ni, Co, As; according to [PERSON] et al., 2004); (**c**): clay content (%).
## 2 Materials and Methods  
### Multirotor UAV as a Control Object and Its Measurement System  
The multirotor UAV can be considered as a multidimensional control plant, being underactuated, strongly non-linear, and highly dynamic with (in general) non-stationary parameters. These features result from its physical structure--especially the use of several propulsion units mounted at the ends of the frame. In addition, measuring, processing, and communication systems are also attached to the middle of this frame--suited for a particular UAV construction. From the perspective of control, the appropriate selection of propulsion units (composed of: brushless direct current motors, electronic speed controllers and propellers) is a key aspect to ensure the expected flight dynamics expressed via thrust (\(T\)) and torque (\(\underline{M}\)) generated by the rotational movement of propellers [39]. By changing the rotational speed, it is possible to obtain the expected position and orientation of the UAV in 3D space, i.e., control of its 6 degrees of freedom (DOFs). The obtained control precision also depends on the quality of sensory information to a large extent. Presently, even in the simplest, low-cost UAVs (Figure 1), in order to determine current position and orientation estimates during the flight (e.g., based on more or less advanced modifications of Kalman filters [6]), the sensory data fusion is used (from 3-axes accelerometer, 3-axes gyroscope, 3-axes magnetometer, pressure sensor, optical-flow sensor, GPS, ultrasound sensor, etc.). In the paper, two sources of measurements are used in the proposed auto-tuning procedure: on-board UAV avionics (for \(roll\) and \(pitch\) angles measurements) and external motion capture system (OptiTrack) (\(X\), \(Y\), \(Z\) position, and \(yaw\) angle). In the UAV autonomous control, to ensure unambiguous description of the UAV's position and orientation in 3D space, the North-East-Down (NED) configuration of the reference system is used, since the on-board measurements are expressed in local coordinate system (\(\mathcal{BF}\)--Body Frame), and the position control, as well as motion capture measurements are defined in the global one (\(\mathcal{EF}\)--Earth Frame). In the paper of [PERSON] et al. [40], one may find a better known, basic information about the mechanisms of conversions, e.g., how the posture of the multirotor (its rotational and translational motion) can be described by the relative orientation between the \(\mathcal{BF}\) and the \(\mathcal{EF}\) with the use of the rotation matrix \(R\in SO(3)\).
## 6 Discussion  
Using ground 3D laser scanning and UAV photogrammetry technology, we identified the rock discontinuity and extracted information of the high and steep cliff on the south side of Laojun Peak, which has the following characteristics:  
1. Multi-source point cloud fusion technology ensures the accuracy and efficiency of rock mass point cloud data acquisition. In past studies, almost all scholars used a single method to obtain three-dimensional point cloud data of rock masses and extract rock discontinuities. For example, [PERSON] [3], [PERSON] [11], and [PERSON] [31] all adopted ground 3D laser scanning technology or UAV photogrammetry technology to obtain 3D point cloud data of rock masses in the study area. However, in most cases, a single way of measuring easily leads to obtaining point cloud data that are incomplete or have poor precision. For example, [PERSON] [10] once pointed out that the accuracy of ground three-dimensional laser scanning technology is higher, but it is greatly influenced by the rock mass discontinuity surface inclination. This is followed by the small-angle laser launch of the rock mass discontinuity, whose data quality is poorer. Point cloud data holes easily appear, and a variety of ways should be used for supplementary testing. Therefore, our proposed multi-source point cloud fusion technology ensures the integrity and accuracy of rock mass point cloud data. However, there are drawbacks to this approach. If the cavity region of the 3D laser scanning point cloud and the distorted region of UAV photogrammetry data are in the same position, the multi-source point cloud fusion method will lose its function. Therefore, in the early stage of measurement, it is necessary to carry out a field survey and make a complete measurement plan.  
Figure 18: Histogram of cliff wall continuity.  
Based on the 3D point cloud, the normal vector of the point cloud is calculated using an improved nearest-neighbor search algorithm and transformed into the rock body yield (dip angle and dip direction). Combined with KDE and DBSCAN clustering algorithms, the rock body discontinuities are grouped dominantly and the yield spacing and continuity of the rock body are calculated. This method can quickly obtain the rock mass production information, and the results are similar to the measured production and meet the accuracy requirements. In addition, this method can greatly reduce the geologists' field workload and lower the time cost. However, this method also has some drawbacks, as [PERSON] et al. [15] pointed out; when discontinuous surfaces are extracted from 3D point clouds and clustered using the DBSCAN algorithm, although the method has good robustness to noisy points, it still needs to combine density peaks and manually set the number of clusters, and the degree of automation and intelligence needs to be improved.
## 2 Results
### SAMM50 Regulates Thermogenic Factors in Beige Adipocytes  
The expression of _UCP1_ was significantly reduced in the shSAMM50 groups compared with the NT group (Figure 5A, \(p\) < 0.01). In contrast, the mRNA level of _UCP1_ was significantly up-regulated in the FLAG-SAMM50 group compared with the FLAG group (Figure 5B, \(p\) < 0.01), along with a decreased intracellular UCP1 in the shSAMM50-2 group but an increase in the FLAG-SAMM50 group (Figure 5C,D). The expression levels of thermogenic genes, including _PPARGC1A_, _DIO2_, _ELOVL3_, _CIDEA_, and _CIDEC_ were down-regulated by SAMM50 knockdown (Figure 6A, \(p\) < 0.005). On the other hand, the overexpression of SAMM50 up-regulated the expression of these thermogenic genes in beige adipocytes (Figure 6B, \(p\) < 0.05). Furthermore, the protein levels of PPARGC1A and UCP1 were significantly suppressed in the shSAMM50-2 group when compared to the NT group (Figure 6C, \(p\) < 0.01), while the overexpression of SAMM50 elevated the protein levels of PPARGC1A and UCP1 in the FLAG-SAMM50 group when compared to the FLAG group (Figure 6D, \(p\) < 0.01).  
Figure 5: _Cont._  
Figure 5: Effects of SAMM50 knockdown or overexpression on the expression of UCP1 in beige adipocytes. (**A**,**B**) The expression levels of _UCP1_ in beige adipocytes infected with shSAMM50. (**A**) and FLAG-tagged SAMM50 (**B**) were analyzed using qPCR. (**C**,**D**) Representative fluorescence images of beige adipocytes infected with shSAMM50 (**C**) and FLAG-tagged SAMM50 (**D**) were stained with DAPI (blue) and anti-UCP1 (green), respectively. The fluorescence levels of UCP1 were quantified using ImageJ software. Results are shown as mean \(\pm\) SEM. \({}^{*}\)_p_ \(<\) 0.05; \({}^{***}\)_p_ \(<\) 0.01; \({}^{***}\)_p_ \(<\) 0.005 compared to the controls.