esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparative Study on the Schedulability of the EDZL Scheduling Algorithm on Multiprocessors
## 4 Comparing EDZL and EDF\({}^{(k)}\)
### Dominance between EDZL and EDF\({}^{(k)}\) Schedulability Test  
We look into the dominance between the schedulability tests for EDZL and EDF\({}^{(k)}\). We found out that the result of the schedulability test for EDF\({}^{(k)}\) is always the same as that of the utilization-based test for EDZL in our experiments. Motivated by this, Theorem 5 proves that the utilization-based test for the EDZL (Theorem 1) and EDF\({}^{(k)}\) schedulability test (Corollary 1) are equivalent.  
**Theorem 5**: _The utilization-based EDZL schedulability test and the EDF\({}^{(k)}\) schedulability test are equivalent._  
Proof.: We rewrite Equation (3) using the definition of \(\tau^{(i)}\) and the assumption that \(u_{i}\geq u_{i+1}\) as follows.  
\[U(\tau^{(m-m^{\prime}+1)})\leq m^{\prime}-(m^{\prime}-1)\cdot u_{m-m^{\prime} +1} \tag{9}\]  
Figure 6: Tightness for each EDZL schedulability test. Demand + Util represents the union of demand-based test and utilization-based test.  
Let \(k=m-m^{\prime}+1\). Then,  
\[U(\tau^{(k)}) \leq m-k+1-(m-k)\cdot u_{k}\] \[u_{k}+U(\tau^{(k+1)}) \leq m-k+1-(m-k)\cdot u_{k}\] \[U(\tau^{(k+1)}) \leq (m-k+1)(1-u_{k})\] \[m \geq (k-1)+\frac{U(\tau^{(k+1)})}{1-u_{k}} \tag{10}\]  
Since both \(m\) and \(k\) are integers, the above equation can be rewritten as follows.  
\[m\geq(k-1)+\left\lceil\frac{U(\tau^{(k+1)})}{1-u_{k}}\right\rceil \tag{11}\]  
If \(\tau\) can be admitted by the utilization-based EDZL schedulability test, there exists \(m^{\prime}\) satisfying Equation (9). Then, Equation (11) holds for \(k=m-m^{\prime}+1\)., i.e., \(\tau\) can be admitted by EDF\({}^{(k)}\) schedulability. Similarly, if \(\tau\) can be admitted by EDF\({}^{(k)}\) schedulability, there exists \(k\) such that Equation (11) holds. Then, for \(m^{\prime}=m-k+1\) Equation (9) holds, i.e., \(\tau\) can be admitted by the utilization-based EDZL schedulability test. Therefore, the utilization-based EDZL schedulability test and the EDF\({}^{(k)}\) schedulability test are equivalent and dominate each other.  
Theorem 5 implies that the set of task sets admitted by the utilization-based EDZL test is the same as the set of task sets that are admitted by the EDF\({}^{(k)}\) schedulability test. This also implies that Demand + Util can admit all task sets admitted by the EDF\({}^{(k)}\) schedulability test. And, as explained in Section 3.2, there exist task sets that the demand-based test can admit but the utilization-based test cannot. Therefore, Demand + Util strictly dominates the EDF\({}^{(k)}\) schedulability test.
## 2 Materials and Methods
### Statistical Analysis  
Total numbers of visible and ovulated follicles, total numbers of oocytes, and embryos were subjected to square root transformation to approximate normal distribution before subjecting to ANOVA. Data observed once a time (results of the first trial 1) including weights of different reproductive organs, numbers of ovarian follicles, ovulation points, oocytes, and embryos were analyzed by a generalized linear model (GLM) of SAS [16]. The same procedure was used for litter size weight and viability variables in the second trial. Repeated measurements including concentrations of serum LH, P\({}_{4}\), and E\({}_{2}\) were analyzed using the MIXED procedure of SAS. The statistical model included the fixed effect of treatment, time of sampling/data collection, and the interactions as well as the random effect of an individual female were considered. Categorical data, expressed as percentages (conception, parturition, and abortion rates) were analyzed using a chi-square test (PROC FREQ). All results were presented as least square mean \(\pm\) standard error (\(\pm\) S.E.M.). The statistically significant differences were accepted from \(p\) < 0.05).  
Figure 2: Experimental planning of trial 2, aiming to determine the effect of gonadotrophin-releasing hormone (GnRH) in different routes of administration on hormonal patterns (by means of successive blood samples; BS) and fertility and reproductive traits after artificial insemination (AI).
## 4 Use of the Controller-Driven OppNet Architecture to Manage Congestion
### Control Layer Data Message  
In the scope of forwarding algorithms based on message replication, one of the forwarding algorithm parameters is the replication limit of the message. The replication limit determines the total number of copies of the message allowed to exist in the network. As seen in Section 2.2, multiple replication strategies exist. For this particular use case, we consider a forwarding algorithm that uses a binary replication scheme consisting in relaying a copy of the message to the contacted node and reducing by half the replication limit of both the node's message and the relayed copy of it.  
The control layer encapsulates the data messages generated by the application layer in the tuple \(g=(\alpha,l,\varrho,\varphi)\). From this tuple, \(\alpha\) is the data message generated at the application layer. \(l\) is the replication limit of the message (in the case of a binary replication scheme, the message can be relayed \(\lceil l/2\rceil\) times). \(\varrho\) is the number of times this particularly message copy has been relayed. This field is incremented each time the message is relayed to the next hop. \(\varphi\) is the _alive_ flag. This flag is set to false to indicate that this message is marked to be deleted in case buffer space is required. This message is not deleted straightforwardly  
Figure 4: Disseminating a directive when two nodes are in range.  
as it could happen that the next contact could be the message destination. Furthermore, it could occur that applying another directive would update this message's replication limit to a value equal to or higher than one, providing the message with more chances to be delivered. With this congestion measure, the messages with the field \(\varphi\) set to _false_ are reactively removed in case of need, but also, in a proactive way, the message is given a chance to be carried along the network while there is no need for buffer space. This strategy requires the node to not consider the messages with the \(\varphi\) field set to _false_ when calculating its buffer occupancy measurement.
# Design and Experiment of Wideband Filters Based on Double-Layered Square-Loop Arrays in the F-Band
## 4 Experiment Results and Discussion  
Based on the simulation results and analysis, the MDM structure with a quartz interlayer sandwiched between two metal layers was fabricated via ultraviolet light photoetching combined with wet etching. The process consists of vacuum copper plating, photoresist spin coating, exposure, development, and wet etching. The sample and a microscope image of several unit cells are shown in Figure 6a. The metallic layers were deposited on a 4 cm \(\times\) 4 cm quartz plate. The measurement setup used in the experiment is shown in Figure 6b. One can see that the transfer characteristics were measured with a N5224A (Keysight Technologies, Inc., Santa Rosa, CA, USA) microwave network analyzer and a VDI WR8.0-VNAX (Virginia Diodes, Inc., Charlottesville, VA, USA) mixer module. Its available spectral range is from 90 GHz to 140 GHz. To accurately measure the transmission spectra, we used a lens to focus EM waves and reduce edge effects, as shown in Figure 6b. The simulated and experimental transmittance spectra are compared in Figure 7. One can see from Figure 7 that the experimental values of \(f_{1}\) and \(f_{2}\) are 105.81 GHz and 127.43 GHz with a transmittance of 0.83 and 0.89, respectively. The experimental results show that the resonant peaks are offset by approximately 1% and the amplitudes of the resonant peaks are approximately 10% lower than the simulation results. The 3-dB bandwidth determined from the experimental results is 32.1 GHz at frequencies ranging from 101.2-133.3 GHz. It should be pointed out that the relative dielectric constant of the quartz substrate is not constant over the F band, which causes the measured resonant frequencies to differ from the simulation results. Amplitude loss at these two resonant peaks could be due to the slightly oblique angles of incident waves through the lens platform. These should be the primary reason that the transmission spectra obtained in the measurements are slightly different from the transmission spectra calculated by FDTD method. Nevertheless, the simulation and experimental results qualitatively verify the validity of the design.  
Figure 5: Resonant frequencies for various misalignments in the axial and diagonal directions.
# Rapid and Precise Zoom Lens Design Based on Voice Coil Motors with Tunnel Magnetoresistance Sensors
## 3 Rapid and Precise Zoom System Design Example  
### System Performance Requirements  
The zoom delay issue in high-magnification zoom optical systems restricts the efficiency of target detection and identification. Rapid and precise zoom technology can effectively address this problem. In the field of drone-based defense, high-magnification zoom optical systems are required to provide wide-area search and target identification capabilities. However, traditional high-magnification zoom optical systems suffer from zoom delays, leading to target loss during the transition between short and long focal lengths. For example, in the 40\(\times\) zoom optical system designed in this study, the traditional zoom drive method requires 20 s to complete a full zoom cycle. Utilizing rapid zoom technology, this lens can achieve wide-area surveillance in the short-focus state and can quickly switch to a long-focus state, enabling high-resolution identification in the long-focus mode.  
Based on the functional requirements of the system, the performance specifications for the VCM-based rapid zoom optical system are as follows.  
1. Focal length: 7.5 mm to 290 mm (40\(\times\));
2. F-number: 1.8 @7.5 mm to 6.5 @290 mm;
3. Full zoom travel time: 0.3 s;
4. Maximum zoom group weight: 30 g;
5. Maximum zoom group travel distance: 38 mm;
6. Zoom group control precision: 5 \(\upmu\)m;
7. Maximum power limit: 18 W.  
Additionally, to ensure compatibility with existing zoom optical lenses, the dimensions of the lens based on rapid zoom technology should not exceed those of traditional zoom technology lenses of the same specifications. Specifically, the radial dimensions of the VCM-based rapid zoom system should not exceed 68 mm \(\times\) 68 mm. The optical layout is shown in Figure 4.  
Figure 3: High-precision positioning control system components.
## 6 Secure Image Monitoring
### Hardware Performance  
Security mechanisms will typically demand additional processing, memory, transmission and energy resources of wireless sensor networks. Additionally, available resources in sensor nodes will directly influence the adoption of particular security mechanisms. Although the initial age of sensor network technology was permeated by low-cost sensors with modest resources, modern sensors bring reasonable processing and memory capabilities at relative low prices. Additionally, sensor motes in the near future will probably be even more affordable. In the security context, more robust approaches might be enabled when more powerful sensor motes are deployed, considerably benefiting secure image transmissions over wireless sensor networks.  
Meanwhile, research efforts have been devoted to enhance sensor network performance with current available technology. In this context, innovative hardware platforms that enhance the efficiency of sensor motes have been proposed.  
In [80], an architecture to construct visual sensor motes is proposed, employing an FPGA (field programmable gate array) platform. According to [80], that architecture is more efficient than other common visual sensor motes, which can be valuable when providing security. In [81], a hardware architecture is defined for efficient DWT coding of sensed images, which are processed using the JPEG2000 codec. Efficient DWT processing can be helpful when performing usual image coding or even selective encryption.  
Cryptography naturally demands \"high\" computing power, which may be insufficiently provided by popular sensor motes, like MICAz[82]. Additionally, such a demand may be aggravated when visual sensors have to gather and compress image data [83; 84]. Sensor motes endowed with 16-bit processors may perform efficient sensing and transmission functions, but specialized modules for image processing are required for higher efficiency, as proposed in [85]. Actually, innovative hardware platforms come as an alternative to support the design and implementation of secure wireless image sensor networks, and the works presented in [80; 81; 83; 84; 85] bring valuable contributions in that direction.
# Social-Aware Peer Selection for Device-to-Device Communications in Dense Small-Cell Networks
## 3 System Description
### Encounter Duration  
Encounter Duration between _Tx_ and _Rx_ is the time when both are located in the coverage area of the same SCBS and encounter each other by getting close enough to be in D2D proximity of each other (i.e, \(D_{Tx,Rx}<d_{th}\)). Encounter duration between _Tx_ and _Rx_ is denoted by \(Y_{(Tx,Rx)}\) and is given by [17]  
\[{}_{(Tx,Rx)}\ =\ \frac{\sum_{i=1}^{e}t_{i(Tx,Rx)}}{\Gamma}, \tag{3}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Friendship Class** & **_SFI Value_** \\ \hline Close friend & \(1\geq SFI\geq 0.75\) \\ Colleague & \(0.75\geq SFI\geq 0.50\) \\ Acquaintance & \(0.50\geq SFI\geq 0.25\) \\ Stranger & \(SFI\leq 0.25\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: _SFI_ values for different friendship classes.  
where \(e\) is the number of encounters, \(t_{i(Tx,Rx)}\) is the \(i_{\rm th}\) encounter time between \(Tx\) and \(Rx\), and \(\Gamma\) is the total sample time, which is the total time during which \(Y_{(Tx,Rx)}\) is measured. The method to compute \(Y_{(Tx,Rx)}\) is illustrated by an example in Figure 2, where \(Y_{(Tx,Rx)}=(t_{1}+t_{2}+t_{3})/\Gamma\). The rectangular boxes represent the activity time of \(Tx\) and \(Rx\), and \(t_{1}\), \(t_{2}\), and \(t_{3}\) represent the time during which both \(Tx\) and \(Rx\) are active simultaneously in each other's proximity and, hence, encounter each other.  
The distance (\(D\)) between DUEs is the most important physical attribute. If we assume that \(Tx\) is currently located at polar coordinates (\(X_{Tx}\), \(\theta_{Tx}\)) while \(Rx\) is at (\(X_{Rx}\), \(\theta_{Rx}\)), where \(0\leq\theta_{Tx}\), \(\theta_{Rx}\leq 2\Pi\); then, the distance \(D_{(Tx,Rx)}\) between \(Tx\) and \(Rx\) can be determined from their current locations [18] using  
\[D_{(Tx,Rx)}=\sqrt{X_{Tx}^{2}+X_{Rx}^{2}-2X_{Tx}X_{Rx}\cos(\theta_{Tx}-\theta_ {Rx})}, \tag{4}\]  
The number of DUEs (\(m\)) also has a great impact on D2D communications. With a lower value of \(m\), the number of friend DUEs are also fewer. Moreover, the DUEs are located far away from each other. Conversely, with the increase in \(m\), the friend DUEs are increased, and the distance among the DUEs is decreased. Hence, we consider \(m\) along with \(\lambda\) and \(D\) in the M\({}_{DP}\).
# Femtosecond Pulse Laser Near-Field Ablation of Ag Nanorods
## 2 Experimental  
Ag NRs were synthesized through a wet-chemical method [30]. First, 25 mL of 0.4 mM AgNO\({}_{3}\) (>99.8%, Sinopharm Chemical Reagent Co., Ltd) solution was added to 25 mL of 2 mM sodium dodecyl sulfonate (>99%, Sinopharm Chemical Reagent Co., Ltd) solution. Subsequently, 4 mL of 10 mM trisodium citrate (>99%, Beijing Harveybio Gene Technology Co., Ltd) solution was added. The mixture was stirred for 80 min at 90 \({}^{\circ}\)C, and the solution became turbid and gray-green.  
Figure 1 shows a schematic of the experimental setup for irradiation of Ag NRs using a femtosecond pulse laser. A 35 fs laser pulse was emitted by an amplified Ti-sapphire laser system, and the repetition rate was 1 kHz, with a central wavelength of 800 nm. Ag NRs, dropped onto carbon film, were irradiated at non-focused femtosecond-pulsed spots with a diameter of 5 mm. The exposure time was controlled using a shutter connected to a computer, and the laser fluence was fixed by an attenuator at 2 mJ/cm\({}^{2}\). The laser polarization and wavelength were adjusted using a half-wave plate and frequency doubling crystal (BBO crystal). Transmission electron microscopy (TEM) images were acquired using an FEI Tecnai G2 F20 operated at 120 kV. The Ag NRs were centrifuged three times and dropped onto copper grids (400 mesh) coated with ultrathin carbon film.  
Figure 1: Schematic of experimental setup for irradiation of Ag NRs using a femtosecond pulse laser. Mirrors (M1, M2). Half-wave plate (HWP).
# Dynamic Interaction Analysis of Long-Span Bridges Under Stochastic Traffic and Wind Loads
## 3 Nonlinear Dynamic Solution Methods for Large-Span Structures  
In examining the dynamic behavior of a long-span bridge under the influence of wind and stochastic traffic loads, the resulting dynamic equations often display pronounced nonlinear features. To tackle this nonlinear coupled vibration issue, this research adopts the Newmark-\(\beta\) method for time-domain integration, coupled with the Newton-Raphson method for iteratively resolving the nonlinear equations. Specifically, the Newmark-\(\beta\) method is employed to discretize the second-order differential equations, enabling the calculation of the system's dynamic response. In parallel, the Newton-Raphson method is used to handle the system's nonlinear characteristics by iteratively approximating the solutions of the nonlinear equations in a step-by-step manner. This combined approach effectively addresses the nonlinear dynamics challenges in vehicle-bridge interaction systems. ABAQUS is adopted to solve the structural dynamic response. In ABAQUS, contact nonlinearity is considered by defining contact pairs and specifying the contact behavior between different parts of the structure. Material nonlinearity is taken into account by selecting appropriate material models that capture the nonlinear stress-strain relationship. Geometric nonlinearity is considered by enabling large deformation analysis, which allows for the accurate simulation of the structure's behavior under significant displacements and rotations.
# Iterative Oblique Decision Trees Deliver Explainable RL Models
## 3 Methods
### DT Training Methods
#### 3.4.1 Episode Samples (EPS)  
The basic approach has been described in detail in [1]. In brief, it consists of three steps:  
1. A DRL agent (\"oracle\") is trained to solve the problem posed by the studied environment.
2. The oracle acting according to its trained policy is evaluated for a set number of episodes. At each time step, the state of the environment and action of the agent are logged until a total of \(n_{s}\) samples are collected.
3. A decision tree (CART or OPCT) is trained from the samples collected in the previous step.
## 1 Introduction
During the few last years, the 3D geovisualization of geographic information created using the above processes and algorithms has been realized through augmented and virtual reality depictions. Augmented reality (AR) was defined by [PERSON] in 1997 [49] as the augmentation of real space by introducing additional information through an artificial medium, such as mobile platforms. Virtual reality is defined as a computer-generated simulation of a three-dimensional world that can be experienced in a believable manner by people wearing special electronic equipment such as headsets with integrated displays or gloves with sensors etc. [50; 51]. In recent years, more and more geographical applications of geolandscaping and analysis use these specific techniques in different fields of application [29; 52; 53; 54; 55]. In geostic promotion and management there are references to augmented and virtual reality applications where users can browse through virtual space and observe specific geological formations remotely [56]. Furthermore, in other cases, users can visit a geosite in situ and receive 3D or 2D information for the area through an application installed on their mobile phone [20; 57; 58; 59; 60; 61; 62]. However, few studies exist in the literature that investigate how the quality of the 3D model of outdoor geological formations affects the data collection, considering the geographical and cartographic scale of the geosite and issues of optical variables on the 3D virtual space. This paper investigates the scale issues related to data acquisition and the scale-variant flight parameters that affect the creation of 3D geovisualizations used in AR maps. The relationship between the geographic and cartographic scale, the data spatial resolution, and the produced 3D models of the geosites are studied in more depth. In addition, the lighting of the 3D models is examined as an optical variable in the 3D space as well as the contribution of the 360\({}^{\circ}\) panoramas as environmental lights. This study's contribution lies in the creation of scale-variant data acquisition flight plans for mapping and depicting geosites in AR. Additionally, it illustrates flight plans for 3D mapping using 360\({}^{\circ}\) panoramas and aerial images for the 3D geovisualization of complex geomorphological structures with intense lighting differentiation. More specifically, the use of panoramas as the key illumination parameter of an outdoor 3D scene of geosite is proposed as it contributes significantly to its photorealistic performance in the 3D virtual space. This ensures the visual improvement of the original 3D models in the AR environment for the 3D geovisualization and mapping of areas with geological interest.
# Time Delay Optimization of Compressing Shipborne Vision Sensor Video Based on Deep Learning
## 3 Compression Experiment of Shipborne Vision Sensor Video
### SVSV Sequence Parameters  
In the process of compressing SVSV, a total of 63 sets of uncompressed SVLC video sequences and infrared camera video sequences in different navigation scenes and RGB color spaces are acquired, and their main parameters are given in Table 1. Meanwhile, in order to adapt to the input characteristics of HEVC, the SVSV color space will be preprocessed and converted to YUV format with a sampling rate of 4:2:0, which reduces part of the color information redundancy. The conversion and inverse conversion processes are shown in Equations (3) and (4).  
\[\left\{\begin{array}{l}Y=0.229R+0.587G+0.114B\\ U=-0.1687R-0.3313G+0.5B+128\\ V=0.5R-0.4187G-0.0813B+128\end{array}\right. \tag{3}\]  
\[\left\{\begin{array}{l}R=Y+1.402(V-128)\\ G=Y-0.34414(U-128)-0.71414(V-128)\\ B=Y+1.722(U-128)\end{array}\right. \tag{4}\]