esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries
### [PERSON], [PERSON], and [PERSON] [29] introduced the ring learning with errors problem.  
**Definition 1** (Decision RLWE).: _For security parameter \(\lambda\), let \(f(x)=x^{d}+1\), where \(d=d(\lambda)\) has a power of 2. For \(q=q(\lambda)\geq 2\), let \(R=\mathbb{Z}[x]/(f(x))\) and \(R_{q}=R/qR\). Let \(\chi=\chi(\lambda)\) be a distribution over \(R\). The DRLWE\({}_{d,q,\chi}\) problem is used to distinguish the following two distributions: the first distribution \((a_{i},b_{i})\in U(R_{q}^{2})\) and the second distribution \((a_{i},b_{i})\in R_{q}\times R_{q}\), where \(b_{i}=a_{i}\cdot s+e_{i}\) with \(s\gets R_{q}\), \(e_{i}\leftarrow\chi\), \(a_{i}\gets R_{q}\). The DRLWE\({}_{d,q,\chi}\) assumption is that the DRLWE\({}_{d,q,\chi}\) problem is difficult._  
The RLWE problem is a variant of the LWE problem. DRLWE is a decision version of RLWE. In the worst case, the shortest vector problem (SVP) can be reduced to RLWE.  
**Theorem 1**.: _Let \(d\) be the power of 2, and ring \(R=\mathbb{Z}[x]/(f(x))\), where \(f(x)=x^{d}+1\), the prime integer \(q=1\) mod \(d\), and \(B=\omega(\sqrt{d\log d})\). There is an efficiently samplable distribution \(\chi\), which can output elements of \(R\) with a length at most \(B\) with overwhelming probability, such that if there exists an efficient algorithm to solve the RLWE\({}_{d,q,\chi}\) problem, then there is an efficient quantum algorithm to solve \(d^{\omega(1)}\cdot Here,(q/B)\) is the approximate worst-case SVP problem for ideal lattices over \(R\)._  
Here, we define the B-bounded distribution as a distribution from sampling size not exceeding \(B\).  
**Definition 2** (Polynomial B-Bounded Distribution).: _Sample \(x\leftarrow\chi\), \(x\in[-B,B]\), then the distribution \(\chi\) on the polynomial is B-bounded, denoted as \(\|\chi\|\leq B\)._
# Combined Use of LoRaWAN Medium Access Control Protocols for IoT Applications
## 4 LoRaWAN Medium Access Control Protocols  
The LoRaWAN regional parameters document [36] describes several aspects of LoRaWAN communication, such as the permitted transmission management strategies and some details on the channel frequencies that can be used. The document also suggests to refer to the specific norms of the region in which the LoRaWAN end-devices operate to obtain authoritative information.  
Figure 3: LoRaWAN message format.  
For instance, in Europe, operation in the 868 MHz region is regulated by the ETSI norms EN 300 220 [40; 41], in the sub-bands specified in the ERC Recommendation 70-03. As far as the channel access strategy is concerned, the ETSI regulations allow the choice of using either a Pure ALOHA MAC protocol with duty-cycle limitation or a polite spectrum access based on LBT AFA. These uncoordinated MAC protocols cannot support real-time traffic, as they do not enforce predictability and do not provide any time or frequency reservation mechanism. Consequently, an end-to-end delay bound cannot be calculated exactly or stochastically [70; 71; 72; 73].
## 2 Materials and Methods  
### Subjects, Apparatus, and Lifting Experiments  
This experiment was conducted on 9 adult males (age: 23.1 \(\pm\) 1.4 years, height: 176.6 \(\pm\) 4.9 cm, weight: 69.3 \(\pm\) 6.7 kg). The subjects were selected based on the questionnaires of the subjects considering general musculoskeletal diseases such as rheumatoid arthritis, osteoarthritis, and mechanical pain in the lower extremities and lumbar joints [25].  
This experiment was approved by the local ethics committee. It was carried out following the guidelines of Sungkyunkwan University. In addition, written informed consent was obtained from all participants before the experiment.  
As experimental equipment, six MCam2 cameras (VICON, Oxford Metrics, Oxford, UK) and 2 OR6-6-2000 ground reaction forces (AMTI Inc., Newton, MA, USA) were used. Each system was operated at sampling rates of 120 Hz and 1080 Hz, respectively [24; 26; 27]. Participants wore a Pedar-X insole system (Pedar Mobile, Novel Electronics Inc., GmbH, Munich, Germany). Data were acquired at 100 Hz [21; 28]. The camera and ground reaction force data were time-series synchronized with a Vicon 460 system. The motion analysis system and the Pedar-X insole system were manually synchronized based on the instance when the subject lifted a box from the ground during a lifting task.  
Optical markers were attached to 16 anatomical landmarks of the lumbar spine and lower extremity joints of each subject based on the modified [PERSON] marker protocol [11; 26]. In addition, one optical marker was attached to the box to distinguish the event of the lifting task. Each subject performed warm-up exercises before participating in this experiment. Preliminary movements were performed to adapt to the experimental environment. For the lifting motion, two postures were performed: squat and stoop. Each lifting motion was performed with boxes (Size: 32 \(\times\) 40 \(\times\) 25 cm\({}^{3}\)) having 3 different weights (4, 8, and 12 kg). The speed of the lifting task was set as normal, fast, or slow. Each speed indicated the normal, fast, and slow speed considered by the subject, respectively. Each subject performed the 54 times of lifting motions (3 repetitions for 18 types of lifting tasks: 2 postures \(\times\) 3 weights \(\times\) 3 speeds). Subjects are asked to lift boxes from ground to the knuckle height [5; 29].
## 3 Earthquake Modelling  
[PERSON]'s dislocation theory [26] has been generally used to study and invert seismic fault displacements. However, it only works under the assumption that the Earth crust is a homogeneous elastic half-space and for near-field observations, as the Earth's curvature and radial heterogeneity are not taken into account in [PERSON]'s equations. This standard dislocation model calculates an analytical solution for ground deformation due to shear and tensile faults. The fault is modelled by a rectangular geometry (defined by length, width, depth, strike, dip and 3-component dislocation amplitude (fake, slip and opening)) which is decomposed into small dislocation patches. This theory will be used to determine the slip distribution in this study.  
From previous models available for the Chi-Chi event in the literature as presented in the introduction of this paper, inconsistencies are observed between rupture processes of the earthquake using one or multiple types of observations. For instance, inconsistencies due to the quality of the data sets: low density of GPS points and low coherence of InSAR, non-negligible residuals between observations and model or the type of dislocation model used introducing overlaps and gaps.  
The proposed method in this paper combines five data types in a joint inverse model and thus requires a precise estimation of relative weighting of each data set. In that objective, the generalized Akaike's Bayesian Information Criterion (gABIC) is used.  
Furthermore, before estimating the relative weights and inverting the data, in order to make the computation feasible and efficient, InSAR observations are often down-sampled to limited numbers of points (from several thousand points to a few hundred). The Quadtree method [27] was used and displacement gradients are used to determine the sub-sampling window size. Before this, a linear trend is removed from the interferograms (to remove the orbital ramp).
## 3 Case Analysis of Storage Tank Leakage Accidents
### Application of the Proposed Method
#### 3.2.5 Data Processing and Parameter Settings for the ANN Model  
According to the mapping rules proposed in Section 2.4.1, 800 sets of data are selected as the training set, 170 sets as the validation set, and 30 sets as the test set. The first 22 columns are extracted as input variables X, corresponding to the 22 basic events, and the 23 rd column, the top event data, is used as the output variable y. Next, the data are standardized using the StandardScaler from the sklearn library for X, and the train_test_split function is used to divide the data into training and testing sets with a test set size of 30 samples and a random seed set of 42. The data are then converted into torch.float32-type tensors and a mini-batch data loader is created with a batch size of 64, where the data are shuffled.  
In terms of parameter settings, the constructed feedforward backpropagation ANN model contains two hidden layers (with 8 and 3 neurons, respectively) and one output layer, as shown in Figure 6. The activation function used is nn.Sigmoid(). Based on the hyperparameter tuning results from Section 3.2.4, the learning rate (lr) is set to 0.1, the batch size is 64, the L2 regularization weight is 0.001, and the loss function is the mean squared error loss function nn.MSELoss(). The optimizer used is the Adam optimizer with regularization, and training stops if the test set's mean squared error does not improve for 10 consecutive iterations.
## 3 Results  
### Etching Behavior  
The etching method employed has been described in greater detail previously [12]. In this work, the temperature profile of a typical etching session appeared as illustrated by Figure 4 below, where the solution reached its peak temperature of just over 340 \({}^{\circ}\)C over the course of about 2 h, after which etching was allowed to continue at a steady temperature for 8-10 h. As the acid solution was heated, several transformations took place due to heat and dehydration into diacid and polyacid forms, the nuances and impacts of which have been discussed elsewhere [15; 16; 17; 18].  
Figure 4: The temperature profile during an etching session was monitored and found to be consistent, plateauing around 343 \({}^{\circ}\)C. Under gentle heat and with limited ventilation, this temperature was self-maintained for long periods of time by the evaporation and condensation of the sulfuric acid in the mixture.  
Figure 3: The effective numerical aperture was determined by measuring the beam diameter (4\(\sigma\) cutoff intensity) at a given distance from the tip of the fiber and then again at a further known distance. From this data, the divergence angle and therefore numerical aperture (NA) could then be determined.  
The etch rate was catalogued as a function of temperature, with the etch rate determined with regard to the rate of reduction in fiber diameter, that is, 2\(\times\) the linear surface etch rate. As may be anticipated, a linear dependence of etch rate upon temperature was observed, with very little etching occurring at and below 270 \({}^{\circ}\)C and a maximum diameter reduction rate of around 5.7 \(\upmu\)m/h observed at the maximum temperature of 343 \({}^{\circ}\)C (Figure 5).  
Provided the sapphire fiber was fully immersed in the etching solution, the surface of the fiber would be etched cleanly and consistently using the described method (Figure 6a). At regions where the fiber exited the solution, deposits would form which were difficult to remove and resulted in an optically unfavorable surface (Figure 6b). This observation provided the impetus for the addition of the aforementioned borosilicate shielding tube, which prevented adherence of these deposits on the sapphire fiber surface at this air-acid interface.
## 4 Experiments and Comparisons
### Experimental Setup  
The computer CPU used for the algorithm test is an Intel Core [EMAIL_ADDRESS] GHz. Additional hardware equipment includes a NVIDIA GeForce GTX 1060 GPU, 16 GB memory and a 1 TB hard disk. The software environment includes matlabr2016a, 64-bit Linux operating system, Cudnn 5.0, CUDA 8 toolkit, Pycharm and Pytorch. We divide the stored training images into a batch of 64 groups, and each batch contains 128 \(\times\) 128 pairs of segmented pictures to go through the network for training. After training, the loss function between restored images and clear images is calculated, and the weight parameters are updated by back propagation, finally the optimized network model is obtained. When the number of infrared image dense connection modules is three, the loss function can be reduced to a more ideal effect. We train a network model at different scales, including images magnified by 2\(\times\), 3\(\times\) and 4\(\times\), and then combine LR and HR pairs at all different scales into a training dataset. For pixels near the image boundary, we fill zero before convolution to keep the same size of all feature maps. The momentum parameter of the network is 0.9, and the weight attenuation is set to \(10^{-4}\). Next, we set the initial learning rate to 0.1, and then reduce it by 10 times for every 25 cycles [23,24]. In order to ensure the rapid convergence of the network, the gradient clip is gradually set within a certain range [\(-\tau\), \(\tau\)], where \(\tau\) is set to 0.4 [25, 26, 27]. The activation function and regularization operation follow each convolution layer, and the negative slope of the activation layer is 0.2 [28, 29, 30]. We trained 90 generations of deep learning according to the above-mentioned set procedure, and the entire process take about 75 h. We observed that the loss function of the 85 th generation network model was the smallest and chose this model for subsequent experiment.
## 5 Communication Protocol Design
### PHY Layer Description
#### 5.1.1 Frame Format  
The packet frame format used by the protocol consists of a PHY header and a PHY payload part, as shown in Table 2. The PHY header contains a fixed two-byte _synchronization pattern_ used for obtaining symbol, frame and frequency synchronization. The PHY payload comprises four fields, where the _source address_ identifies the sensor node (the PN-sequence assigned to the sensor node can also be used for identification), the _sequence number_ provides a means to ensure frame sequence integrity, _payload data_ carries the actual payload and _CRC 16_ allows one to check the integrity of the individual frame.  
Figure 4: Measurement setup with four Marmote SDR sensor nodes (transmitters), a USRP N210 and desktop computer base station (receiver).
## 3 Interactions of Antitoxins and Toxins with Cellular Components
### Interactions Involving Toxins
#### 3.1.5 Interfering with Cell Division  
The _cbeA-cbtA_ system is the only representative of type IV TA systems [19]. The CbtA toxin inhibits the polymerization of MreB and FtsZ in _E. coli_, two essential proteins involved in the cytoskeleton and cell division, respectively [28]. _In vitro_, CbtA inhibits ATP-dependent polymerization of MreB and GTP-dependent polymerization of FtsZ. Interestingly, the two functions of CbtA can be split: the amino-terminus is responsible for FtsZ interaction, while the carboxy-terminus is for MreB interaction. To our knowledge, this is the first example of a toxin belonging to a TA system that interacts with two distinct targets.
## 2 Materials and Methods
### Plant Yield and Nitrogen Use Efficiency  
For each of the plots for the two crop cycles, the economic yield of the maize, which are the fruits, otherwise known as ears, were weighed after harvest at 70 d after seeding. The shoots (stem, leaves, and ears) in each plot were removed by cutting the stem at 0.1 m from the soil surface for plant biomass measurements. Therefrom, the stem, leaves, and ears were separated and dried in a convection oven until constant dry weight was achieved. After grinding the dried samples, the plant total N concentration was determined using the LECO CHNS Analyzer ([PERSON]@Micro CHNS, LECO, St. Joseph, MI, USA). Nitrogen taken up by the stem, leaves, and ears were determined by multiplyingtheir contents with the dry weight of the plant parts. Urea-N use efficiency of the plants was determined using two indices reported in [8]. These two indices are agronomic efficiency of applied N and crop recovery efficiency of applied N. Equations (i) and (ii) below were used to calculate the agronomic and nitrogen recovery efficiencies of the applied nitrogen, respectively. Five ears were randomly selected for each plot to count the number of grains per ear. The control treatment (T0) was used in calculating N use efficiency to factor out the contribution of N by soil only [7].  
(i) \(\mathrm{AE_{N}}\) = Agronomic efficiency of applied N = (Y\({}_{\mathrm{N}}\)\(-\)Y\({}_{\mathrm{0}}\))/F\({}_{\mathrm{N}}\) (kg yield increase/kg N applied)  
(ii) \(\mathrm{RE_{N}}\) Crop recovery efficiency of applied N = (U\({}_{\mathrm{N}}\)\(-\)U\({}_{\mathrm{0}}\))/F\({}_{\mathrm{N}}\) (kg increase in N uptake/kg N applied)  
where: Y\({}_{\mathrm{N}}\)--Crop yield with applied N; Y\({}_{\mathrm{0}}\)--Crop yield of control treatment without N; F\({}_{\mathrm{N}}\)--Amount of fertilizer N applied; U\({}_{\mathrm{N}}\)--Plant N uptake for a plot with applied N; U\({}_{\mathrm{0}}\)--Plant N uptake for a plot without N application.
# Analysis of 3D Printed Dielectric Resonator Antenna Arrays for Millimeter-Wave 5G Applications
## 3 Millimeter-Wave DRA Array for 5G Systems
### Fabrication and Measurements
The measured radiation patterns in both the xz- and yz-planes generally matched the simulated results, as shown in Figure 18, which indicates agreement between simulation and measurement. However, some discrepancies could still be observed. For example, there is an 8% shift to a lower band in the first resonant frequency, and the measured realized gain at the broadside  
Figure 14: Zoom-in view of the top and bottom surfaces of the FDM 3D-printed mmWave DRA. (**a**) top view, (**b**) bottom view. Figure 15: 2D cut surface profile of the FDM-printed mmWave DRA. direction is about 2 dB lower than the simulated one. Differences in dimensional parameters could be part of the reason. Despite this, FDM 3D-printed DRAs at 5G mmWave frequencies, fulfill design requirements, suggesting FDM as a viable option for DRA production. Figure 16: 3D side view and 2D cut surface profile of the FDM-printed mmWave DRA element. Figure 17: Simulated and measured matching performance of the FDM 3D-printed DRA array with in-phase feeding network. Figure 15.0 Simulated and measured radiation patterns of the FDM 3D-printed DRA array at 26.8 GHz. (**a**) xz- and (**b**) yz-plane.
## 2 Model Building
### Description of the Problem  
The university course scheduling problem (UCSP) is a complex combinatorial optimization problem, especially in the case of combined courses. During the course scheduling process in higher education institutions, various factors such as the teaching plans, courses, instructors, administrative classes, classrooms, teaching classes, and time slots are involved. These factors are interconnected and constrain each other. The teaching plan [22] is primarily formulated by the school's academic dean based on the actual conditions of the school and college, including components such as the courses, instructors, administrative classes, and teaching classes. Specifically, a course for a certain administrative class taught by a particular instructor in a specific teaching class is referred to as a teaching event. The UCSP can be represented as assigning appropriate class times and classrooms to \(n\) teaching events. Since our scheduling scope was a semester, we decomposed the semester into a set \(K\) of weekdays within a week, a number \(W\) of weeks, and a set \(P\) of time slots in a day. For demonstration purposes, we divided a day into 5 time slots and assumed that classes were held on 5 days a week, resulting in 25 time slots available for scheduling [33].  
The solution to this problem is subject to both hard and soft constraints. To enhance the practicality of the scheduling, the following restrictions are considered:  
Hard constraints:  
In the same time slot, a teacher can teach only one course;  
In the same time slot, a classroom can accommodate only one course;  
In the same time slot, a class can be scheduled for only one course;  
The scheduling process must meet the time requirements for each course;  
The classroom assigned to a course must be able to accommodate all students, especially in the case of combined classes;  
When a course is a combined class, multiple administrative classes need to attend the same course at the same time and in the same classroom;  
Special courses require assignment to specific classrooms, such as laboratories for experiments, music rooms, and chemistry labs;  
When the time slot of a teaching event is fixed, the time slot for that teaching class cannot be changed;  
The course hours need to be completed within the specified weeks.  
Soft constraints:  
Distribute the same course evenly throughout the week;  
Distribute the courses for each administrative class evenly throughout the week to avoid scheduling too many courses on the same day, which could lead to an excessive burden on students;  
Avoid having too many teaching hours for instructors within a single day to prevent excessive concentration;  
Strive to accommodate the preferred teaching times of the instructors;  
Aim to optimize the utilization of classrooms and equipment to avoid resource wastage.
# A Distributed RF Threat Sensing Architecture
## 3 Proposed Approach
### Sensor Calibration  
#### 3.2.1 Calibrating the Logarithmic Amplifier  
Before operation, the logarithmic amplifier-based sensor requires an initial calibration. Such a calibration needs to be performed only once per module, after which the calibration values are kept permanently in a database and recalled whenever the module is operating.  
To perform the calibration, a Lab Brick Signal Generator from Vaunix was used, LSG-602, measured with an R&S FSH8 calibrated spectrum analyzer. The signal generator was used to inject a signal within the desired frequency range of the two sensors developed and utilized for this work.  
In our case study, the first sensor utilizes a filter that ranges from 2340 MHz to 2530 MHz, while the second sensor utilizes a filter with a frequency range of 5490 MHz-5895 MHz. Hence for calibration purposes, we used signal generator frequencies within the two frequency ranges.  
The calibration power levels at the logarithmic amplifier inputs are set initially within the linear performance of the dynamic range of the logarithmic amplifier, as described in the manufacturer's device data sheet. The received signal level range at the input of the logarithmic amplifier is then estimated at the input of the antenna by considering the gain of the used antennas, the gain of the low-noise amplifier, and any passive losses. During overall RF chain calibration, all RF gains and RF passive components losses of the RF chain are taken into consideration.  
During the logarithmic amplifier calibration, the sensor application collects a large number of sample measurements (adc ticks), and the average voltage output value (VOUT) is set to construct the linear slope of each module (Figure 3).  
Figure 2: RF sensor to SOC connectivity outline.