esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Plant Material, Harvesting Site, and Sample Preparation  
_E. umbellata_ fruits were collected in the germplasm collection of the Department of Agricultural, Forest and Food Sciences of the University of Turin (DISAFA), located in Chieri (45\({}^{\circ}\)02\({}^{\prime}\)26.3\({}^{\prime}\) N, 7\({}^{\circ}\)50\({}^{\prime}\)16.8\({}^{\prime}\) E, at 305 m a.s.l.), Piemonte (northwest Italy). Berries were harvested manually (500 g for each replication) at the beginning of November 2018 and randomized from three different trees, so that each tree represented one biological replication. The ripening evaluation was based on pericarp color and fruit development. This work is a preliminary research on the autumn olive; therefore, the investigation did not focus on a single genotype, but rather on the entire _E. umbellata_ species. The climate of the area is temperate and for 2018 the average precipitation recorded a value of approximately 770 mm. The soil at the germplasm repository is loam-clay.  
After the harvest, berries were immediately stored in refrigerated containers and sent to the laboratory at the DISAFA in Grugliasco (Province of Turin). Then, selection was performed in order to discard any defective fruits from analysis. Finally, the samples were separated into two equal portions. The same day of harvest, physio-chemical parameters were assessed using one portion. The other portion was stocked for one day at 4 \({}^{\circ}\)C and 95% relative humidity and further used to extract the bioactive compounds.
## 4 System Power Consumption Analysis
The average power consumption is the sum of the average power consumption in each state, which was obtained by multiplying the total power consumption in each state by the time weight, where the time weight = time duration/cycle time. In Table 3, it can be seen that the average power consumption of the sensor detection node was 1.284 mW in the intermittent operation mode, the internal working voltage of the sensor node was 3.3 V, and the weighted-average working current was 0.389 mA, which would allow the sensor detection unit to operate continuously for about 1.5 years using a lithium battery with a nominal voltage of 3.7 V and a battery capacity of 5 Ah. If the concentration of methane in a sewer exceeds the safety limit (usually, the methane concentration is set to 1%, which is 20% of the lower explosive limit), the system will enter the continuous operation mode. In this mode, the system continuously monitors the gas concentration. The sensor detection node monitors the condition of the sewer every second, and the Zigbee communication unit uploads data every minute. During this period, the system will issue a message prompting staff to take measures to reduce the concentration of hazardous gases. If the concentration of methane continues to increase to 2.5%, which is 50% of the lower explosive limit, the sensor node will send out a warning signal and the gateway alarm device in the area in which the sewer is located will send out an audible and visual alarm. If the concentration of methane is reduced to below the safety limit, the alarm is cleared and the system reverts to the intermittent operation mode.
# Hard SyDR: A Benchmarking Environment for Global Navigation Satellite System Algorithms +
## 1 Introduction
### Prior Work  
In prior work [9], we introduced the concept of our SDR named \"SyDR\", standing for \"SDR with Python\", and designed it for GNSS processing. We analyzed existing GNSS SDRs and the benefits of our architecture. The objectives of SyDR are (1) to be a receiver described in a high-level language, in our case Python 3; (2) to be open-source [20]; and (3) to serve as a playground for algorithm benchmarking. Being aware of the significant runtime overheads of Python [21], we already foresaw its conversion to a lower-level language (e.g., C or Rust) and its eventual implementation on hardware such as FPGAs.  
We first addressed this conversion in [22] by outlining our workflow for porting the computation-heavy parts of SyDR into HLS-compatible C/C++ code [23], the targeted hardware architecture, and its integration into Python using PYNQ [24]. We motivated moving from software to hardware with three potential advantages, of which this paper retains two: (1) enable the use of AxC techniques only available in hardware [10; 11] and (2) provide more realistic power and energy consumption estimates. Our focus on delivering a proof-of-concept solution has led us to disregard the third potential--processing acceleration--for now. We argue that this does not take away from the results presented herein, as neither absolute processing speed nor acceleration were the main aims of SyDR.
## 4 Analysis of Experimental Results
### Precise Identification Technology for Emergency Airdrop Stations
#### 4.2.2 Experimental Platform  
Table 7 shows the experimental hardware setup: an Intel(R) Xeon(R) Platinum 8255C CPU (2.50 GHz), and model training was performed on a GeForce GTX 3080 with 10 GB of graphics memory and 40 GB of system memory. The experiments were run on an Ubuntu 20.04 operating system, programmed in Python 3.8.0, and built based on the Pytorch 2.0.0 framework, which includes CUDA 11.8 for processing power enhancement. Uniform parameters were used in the training phase to ensure the validity of the comparison experiments. The experimental dataset is 1189 and the input image size is 640 \(\times\) 640. The optimizer selects SGD and the learning rate is set to 0.01. The momentum is set to 0.937; the number of iterations is 80; the batch size is 8; and no pre-training weights are loaded.
## 3 Parallel Computation and Grid-Overlapping Interface
### Domain Decomposition Strategy  
As described in the introduction, the numerical simulation for cavitation flow requires expensive computational costs given its physical issues. Parallel computing based on domain decomposition is used to reduce the numerical cost. The domain decomposition methods divide the entire computational domain into smaller domains that are independent of each other and communicate through the interface boundary between neighboring subdomains, and thus, the methods are suitable for parallel computing.  
It is important for the overall efficiency of parallel computation to distribute the number of grid points over the subdomains based on the performance of the processor for each subdomain, which is termed load balancing. The most ideal domain decomposition method is that where each processor with an equivalent performance solves the equally distributed subdomain. Subsequently, the performance of numerical simulation is maximized by eliminating the load unbalance that may otherwise occur between the processors. Additionally, the arrangement of subdomains is also critical, and the interface boundary that is parallel to the mean flow is generally preferable because a severe gradient flow can lead to a numerical error during communication between subdomains through the interface boundary. However, with respect to a problem including complex geometries, it is difficult to maintain a lower load on each processor by simultaneously increasing the number of subdomains and the preferable interface boundary lines between the subdomain. In the study, a parallel computation based on the grid-overlapping methods is proposed to resolve the issue by increasing the accuracy of information exchange through the interface boundary between the subdomains.  
Figure 1 shows the schematic concept of serial and parallel computations based on single and multidomains, respectively. The single domain consists of interior computational points (black) and boundary points (white), while multi-subdomains consist of interface boundary points (red circles) in addition to the two types of points.
# A Design for SDN-Based Identifier-Locator Separation Architecture on IoT Networks
## Related Work
### Network-Based Identifier-Locator Separation Schemes  
In contrast, an LISP is a representative network-based ID-LOC separation protocol. An LISP enables the separation of IP addresses into two new numbering spaces. One is the endpoint identifiers (EIDs) that are not globally routable, and the other is routing locators (RLOCs) that are globally routable. Also, LISP defines an encapsulating mechanism for the LISP routers to transmit packets using EIDs across a network infrastructure that uses RLOCs. The routers are responsible to lookup the mappingbetween EIDs and RLOCs. In an LISP, packets to the destination through Ingress Tunnel Routers (ITRs) and Egress Tunnel Routers (ETRs). ITRs and ETRs are used as tunnel start and end points [8].  
A DHT-based identifier-locator mapping scheme (DHT-MAP) was proposed to resolve an LOC for a flat ID [16]. The authors used a DHT-based system, which is a modified version of the CAN system. The CAN is a DHT-based network that uses to map \"keys\" onto \"values.\" The network consists of a customer network for forwarding packets and an overlay network using CAN for maintaining ID-LOC mapping. In this scheme, every autonomous system (AS) maintains one or more resolvers to store EID-LOC mapping. Network-based protocols, however, require the tunneling for forwarding packets. Because the use of tunneling needs an encapsulation and a decapsulation mechanism for processing the packets, it consumes more bandwidth and processing overhead.
## 3 Results and Discussion
### Sample Characterization
#### 3.1.3 Thermal Analysis  
The glass-forming ability and glass thermal stability are the parameters of interest that demonstrate the ability of the proposed melt to resist crystallization when subjected to temperature variations. The characteristic temperatures, glass transition temperature, \(T_{g}\), crystallization temperature, \(T_{c}\) and melting temperature, \(T_{m}\), used to estimate the glass-forming ability and glass thermal stability are identified from thermal analysis. Thermal events, endothermic or exothermic reactions, occur upon heating the glass and are identified through DSC [31]. The DSC thermogram of quenched MgB4O7:Zn glass, identifying  
Figure 1: Typical XRD spectrum of MgB4O7:Zn glass.  
Figure 2: Surface morphology of MgB4O7:Zn glass observed by field emission scanning electron microscopy (FESEM).  
the thermal events of endothermic reaction (phase transition and melting) and exothermic reaction (crystallization), is depicted in Figure 3. The glass-forming ability, \(T_{g}\), was estimated using the Kauzamann relation, given as Equation (5) [32]:  
\[T_{g}=\frac{T_{g}}{T_{m}} \tag{5}\]  
where \(T_{g}\) is the glass transition temperature and \(T_{m}\) is the glass melting temperature.  
The glass thermal stability, \(H_{g}\), resistance to nucleation upon heating, combining the nucleation and phase transformation growth aspects, was estimated using [PERSON]'s assumption given as Equation (6) [33]:  
\[H_{g}=\frac{T_{c}-T_{g}}{T_{m}-T_{c}} \tag{6}\]  
where \(T_{g}\) is glass transition temperature, \(T_{m}\) is glass melting and \(T_{c}\) is nucleation temperature. The smaller \(T_{m}-T_{c}\) values retain nucleated crystal growth, whereas, higher \(T_{c}-T_{g}\) values delay the nucleation process.  
The glass transition temperature, \(T_{g}\), glass melting, \(T_{m}\), and nucleation temperature, \(T_{g}\), of the synthesized glass were identified as 540 \({}^{\circ}\)C, 721 \({}^{\circ}\)C and 882 \({}^{\circ}\)C, respectively. The proposed mixture demonstrated good glass-forming ability, 0.61, within the identified range, \(0.5\leq T_{g}\leq 0.66\)[34]. Glasses with \(H_{g}\leq 0.1\) are hard to fabricate, requiring elevated cooling rates, whereas glasses with \(H_{g}\geq 0.5\) are relatively easy to fabricate with standard quenching rates. The synthesized glass demonstrates excellent glass thermal stability at 1.62 [33].
## 1 An Overview of the Topic and Its Ramifications  
### Introduction  
Guided waves represent a vast class of phenomena in which the propagation of collective excitations in various media is steered in required directions by fixed (or, sometimes, reconfigurable) conduits. Arguably, the most well-known and practically important waveguides are single-mode and multi-mode optical fibers [1; 2], including their more sophisticated version in the form of photonic crystal fibers [3] and hollow metallic structures transmitting microwave radiation [4]. Light pipes, in the form of hollow tubes with reflecting inner surfaces, are used in illumination techniques. On the other hand, medical stethoscopes offer a commonly known example of a practically important acoustic waveguide. New directions of studies in photonics are focused on waveguides for plasmonic waves on metallic surfaces [5; 6; 7] (which provide the possibility of using wavelengths much smaller than those corresponding to the traditional optical range, and thus offer opportunities to build much more compact photonic devices) and on the other hand, on the guided transmission of terahertz waves, which also have a great potential for applications [8].  
Outside of the realm of photonics (optics and plasmonics) and acoustics, wave propagation plays a profoundly important role in many other areas; accordingly, waveguiding settings have drawn a great deal of interest in those areas as well. In particular, as concerns hydrodynamics, natural waveguides--which may be very long--exist for internal waves propagating in stratified liquids (e.g., in the ocean) [9]. Various settings in the form of waveguides for matter waves are well known in studies of Bose-Einstein condensates in ultracold bosonic gases [10; 11]. In solid-state physics, guided propagation regimes for magnon waves in ferromagnetic media are a subject of theoretical and experimental studies [12]. In superconductivity, long Josephson junctions are waveguides for plasma waves [8; 13]. The significance of waveguiding in plasma physics is also well-known; e.g., Ref [14; 15; 16].  
Below, a very brief overview of basic theoretical models and experimental realizations of various physical implementations of the waveguiding phenomenology is given. The text is structured according to the character of the guided wave propagation: linear or nonlinear and conservative or dissipative, as well as according to the materials used in the underlying settings, natural or artificial.  
This presentation definitely does not aim to include an exhaustive bibliography on this vast research area. References are given chiefly to review articles and books summarizing the known results, rather than to original papers where the results were first published. However, in some cases original papers are also cited if it is necessary in the context of the presentation.
## 4 Experiment
### Datasets and Data Preprocessing  
Two datasets were used in this study, namely, satellite remote-sensing data and PV station data. The satellite data were the L1 grid data from Himawa-8, a geostationary satellite launched in 2015 by the Japan Meteorological Agency to provide weather forecasts and typhoon and storm reports for Japan, East Asia, and the Western Pacific. The detection range of Himawa-8 is 60\({}^{\circ}\) N, 160\({}^{\circ}\) E, and 80\({}^{\circ}\) W, with a spatial resolution of 0.05\({}^{\circ}\), which corresponds to about 5 km on the ground, and the temporal resolution is 10 min. The PV station data contains local measurements from three real PV stations at different latitudes and longitudes in Hebei, China. Each station records meteorological factors (including global and diffuse irradiance, temperature, wind direction and speed, and air pressure) and PV power records at 15 min intervals.  
We set the temporal resolution as 30 min to harmonize the time intervals of these two datasets. The satellite remote-sensing data were processed into 40 \(\times\) 40 cloud images centered on the latitude and longitude of the PV station. The satellite data were sampled from July 2018 to June 2019 to align with that of PV station data. For each day, the data with a time range from 7:00 to 19:00 (UTC +0800) were used. We divided these two datasets into a training set, a validation set, and a test set in the ratio of 8:1:1 after arranging them to ensure the test period covers multiple seasons. Before input to the model, the data were normalized using standardization to eliminate the inconsistency of the magnitude of each dimension.
# Modeling the Trip Distributions of Tourists Based on Trip Chain and Entropy-Maximizing Theory
## 4 The Trip Distribution Forecasting Model Based on Trip Chains
### Trip Chain Constraints  
#### 4.1.1 Visiting Volume Constraint of Stay Points  
The trip volume of a trip chain is normally distributed in the involved traffic zones. This means its value is constrained by the amount of produced and attracted trips in the origin and destination zones. By dividing scenic spots and transportation hubs into many traffic zones, the amount of tourist production and attraction in each traffic zone could be easily inferred based on the collected number of tourists in scenic spots and the traffic volume of transportation hubs. This enables us to define restrictive conditions (i.e., constraints) on the amount of tourist production and attraction of origin and destination zones of a trip chain. Suppose every tourist departure from the traffic zone \(m\), and the selected trips choice set is \(H\), then the visiting volume constraint of stay points of a trip chain can be defined as Equation (1):  
\[\left\{\begin{array}{l}O_{m}=\sum\limits_{H\in H^{H}}S_{mH},\ m=\{1,2, \ldots,M\}\\ D_{n}=\sum\limits_{j=1}^{J}\sum\limits_{m=1}^{M}\sum\limits_{(H\in H^{H}|n=n _{j})}S_{mH},\ n=\{1,2,\ldots,N\}\end{array}\right. \tag{1}\]  
where \(O_{m}\) means the produced trip volume of origin zone \(m\); \(D_{n}\) is the number of tourists that arrive at destination zone \(n\); \(H^{a}\) is the destination zone choice set of tourists, and \(S_{mH}\) denotes the volume of trips that select H as the choice set for trip chains which start from origin zone \(m\). Since every trip chain starts from an origin zone, so the number of tourists of origin zone \(m\) (i.e., \(O_{m}\)) is the sum of trip volumes of all the trip chains that start from origin zone \(m\). Similarly, the number of tourists of destination zone \(n\) (i.e., \(D_{n}\)) is the sum of tourists of the trip chains that start from the origin zone \(m\) and take the traffic zone \(n\) as the \(j\)th destination zone, i.e., the number of tourists whose trip chains start from origin zone \(m\) and pass through the traffic zone \(n\).
# Accurate Landmark Localization for Medical Images Using Perturbations
###### Abstract  
Recently, various studies have been proposed to learn the rich representations of images during deep learning. In particular, the perturbation method is a simple way to learn rich representations that has shown significant success. In this study, we present effective perturbation approaches for medical landmark localization. To this end, we report an extensive experiment that uses the perturbation methods of erasing, smoothing, binarization, and edge detection. The hand X-ray dataset and the ISBI 2015 Cephalometric dataset are used to evaluate the perturbation effect. The experimental results show that the perturbation method forces the network to extract richer representations of an image, leading to performance increases. Moreover, in comparison with the existing methods that lack any complex algorithmic change of network, our methods with specific perturbation methods achieve superior performance.  
artificial intelligence; landmark localization; context feature learning image perturbation +
Footnote †: journal: Medical Imaging  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-00000-00000-00000-00000-00000-00000-00  
anatomical context (AC) loss by considering the statistical distributions of distance and angle between landmarks.  
The main reason for the high performance of deep learning lies in learning rich representations [12]. The most straightforward way of representation learning is to use numerous hidden layers, deepening the network. Another effective way of learning richer representations that is popularly used in deep learning is regularization, such as data augmentation and dropout. Recently, a new approach was proposed that perturbs an original input image with the aim of hindering the learner from focusing only on local features, instead enabling the learner to capture semantically global features. There are a variety of techniques for perturbing images, such as adding noise, erasing, and smoothing of patches of an input image. There are abundant reports of successfully applying image perturbation to improve the classification performance [13; 14].  
Despite the broad applications of image perturbation, only a few such applications can be found in the landmark localization task. To the best of our knowledge, ref. [11] is the first and only attempt to apply smoothing perturbation to localize the landmarks for cephalometric images. This fact motivates us to explore a broader class of perturbation schemes, and to identify the optimal scheme. Our experiment uses region elimination, smoothing, binarization, and edge detection as perturbators. In the experiments on hand X-ray images, the hybrid scheme showed the best accuracy. Randomizing the location and size of the patches to be perturbed enhanced the performance.  
Section 2 presents an overview of the related works, whereas Section 3 describes the proposed methods. Section 4 presents our experimental results, and Section 5 discusses and concludes the paper.