esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Iterative Scheduling Algorithm for Throughput Optimization
### UAV Trajectory Subtask Optimization  
The first sub-optimization problem block focuses on UAV path planning, with full knowledge of external information acquired in the previous iteration round, including resource allocation and environmental states. The sum of \(R_{i}\) stands for the objective function, namely the total throughput of the A2G communication links.  
\[\begin{split} Objective&=\sum R_{i}\\ &=\sum B*log_{2}(1+SINR)\\ &=\sum B*log_{2}(1+\frac{P_{ug}(u_{i},e_{j})}{\sigma^{2}}).\end{split} \tag{15}\]  
As the UAV location changes, the parameters of the A2G communication channel model and the distance between UAV base station and ground users also change, which can be determined by UAV position and known environmental information. Environmental parameters also depend on the UAV's location, thus influencing the SINR value of communication links. Therefore, when fixing the other two sub-optimization blocks, the objective function can be regarded as a function of the UAV's position vector.  
To design the location optimization block, constraints of acceleration and energy limitation are considered, which are performed as unequal constraints.  
\[\max_{u_{n},v_{n}} \sum_{i}^{K}R_{i} \tag{16}\] \[s.t. \left\{\begin{array}{l}\sum_{n}^{N}\sum_{i}E_{i,n}^{C}+\sum_{n}E_{ n}^{F}\leq E_{max}\\ \mathbf{0}\leq u_{n}\leq[x_{max},y_{max}]\\ -\pi\leq a_{n}\leq\pi\\ v_{n}\leq v_{max}\end{array}\right.\]  
Decision variables \(u_{n}\) and \(v_{n}\) represent sets of the two-dimensional location and speed of UAV base station at all time slots, while \(K\) and \(N\) represent the total number of moving ground users and communication time slots. \(\Delta T\) is the interval of each time slot and \(P_{0}\) counts for typical transmission power of UAV; therefore, \(\sum_{n}^{N}\sum_{i}E_{i,n}^{C}\) represents energy consumption of communication, while \(\sum_{n}^{N}E_{n}^{F}\) is flight energy consumption. \(u_{n}\) stands for location of UAV at time slot \(n\), and \(a_{n}\) indicates the direction of UAV.
## 2 Material and Methods
### RNA Extraction and Gene Expression Analysis Using Quantitative Real-Time PCR (qPCR)  
The soybean seeds (24 h soaked) from unprimed and magnetoprimed seeds were crushed in liquid nitrogen, and total RNA was extracted using RNAiso Plus reagent (TaKaRa, Kusatsu, Japan). The genomic DNA contamination was removed by treating all samples with DNase I (Thermo scientific, Waltham, MA, USA) following the manufacturer's protocol. The integrity of all RNA samples was confirmed on 1.2% (\(w/v\)) denaturing agarose gel electrophoresis followed by RNA quantification using NanoDrop(tm) 2000 (Thermo Scientific, Waltham, MA, USA). Following this, the cDNA was synthesized with approximately 2 \(\mu\)g total RNA template and oligo (dT)\({}_{18}\) primer by using PrimeScript(tm) 1 st strand cDNA Synthesis Kit (TaKaRa Kusatsu, Japan) following manufacturer's instructions.  
The qRT-PCR reaction (10 \(\mu\)L) was set using PowerUp(tm)SYBR(tm)green master mix (Applied Biosystems, Waltham, MA, USA) and was performed in StepOnePlus(tm)Real-time PCR system (Applied Biosystems). The amplification of each gene in the PCR reaction was performed using initial denaturation at 95 \({}^{\circ}\)C for 10 min, followed by 40 cycles of 95 \({}^{\circ}\)C for 10 s, 58 \({}^{\circ}\)C for 30 s, and 72 \({}^{\circ}\)C for 30 s. Further, dissociation curve analysis was performed from 60 \({}^{\circ}\)C to 95 \({}^{\circ}\)C with an increment of 0.3 \({}^{\circ}\)C/s for data acquisition. The specificity of PCR products amplified from each primer pair was confirmed through dissociation curve analysis. The Elongation factor-1-alpha (_Glyma.19G052400_) and Tubulin alpha-2 (_Glyma.20g27280_) genes were used as internal control (Table 2) and their Ct values were used to normalize the expression of target genes. The relative expression level of genes in fold change with respect to UP samples of control and control + NaCl was calculated using the 2\({}^{-\Delta\Delta\)CT method [43].
## 3 Results and Discussion
### Evaluation Index System Construction
#### 3.1.3. Index Description
* Brand image of the supply chain: This refers to the degree of notoriety and the reputation of a specific brand in the fresh agricultural produce supply chain platform sector. The brand image of the supply chain reflects the personality of the supply chain and the degree to which it is recognized. It is a visual display of the strength and characteristics of the fresh agricultural produce supply chain platform and the core member companies of the supply chain. * Coordination of information flow  
The coordination of information flow refers to the timeliness, accuracy, and adequacy of the supply chain in providing relevant information to customers and the level to which it provides a rapid response to problems. Information sharing is an important element of fresh agricultural produce supply chain platform coordination, and information asymmetry can no longer be used in supply chains as a core component of long-term competition. Cooperation among supply chain members is based on the continuous exchange and sharing of information among all parties, and the competitiveness of the supply chain cannot be achieved without the timeliness and accuracy of information sharing among the members of the supply chain. The following key indexes were included:  
* Frequency of information exchange: This refers to the number of times consumers and service (products) providers in the fresh agricultural produce supply chain platform transmit and circulate information during the operation cycle. It reflects the cooperation and coordination base relationships present across the fresh agricultural product platform supply chain, and it is the basis for rationalizing the coordination of logistics and capital flow in the supply chain system. * Speed of collaborative response: This refers to the level of the flexible collaborative response of the supply and demand sides of the fresh agricultural produce supply chain platform to cope with problems that arise during the process of cooperation. It reflects the efficient and flexible collaborative ability that is present between the members of upstream and downstream enterprises in the supply chain, between different departments within member enterprises, and between employees of member enterprises. * Information processing reliability: This refers to the ability to provide reliable and accurate information during operations, thus reflecting the quality of information processing; therefore, reliable and accurate information can effectively guide decision making in fresh agricultural supply chain systems. * Level of information system: This reflects the level of completeness and sophistication of the information system and facilities for providing customer service. Moreover, it reflects the ability to provide important information related to fresh agricultural products and the supply chain. The more adequate the information provided, the higher the level of service quality.
## 4 Security Approach
### Trusting the Mapping  
By storing the public key at only one administrative domain (region) in the mapping system, however, the user heavily depends on the trustworthiness of that particular region. In case the mapping service provider collaborates with an attacker, it could send a wrong or manipulated public key to the client. Therefore, any security functionality based on the public/private key principal would be rendered useless. Even worse, the client considers the connection to be secure while in fact talking directly to the attacker.  
To address this issue, we suggest to divide the public key into \(n\) pieces and distribute those to various independent regions. To calculate the public key only \(k\) out of \(n\) pieces are required, using the threshold cryptography, as explained in Section 2.2. Figure 4 illustrates an example with \(n=4\) and \(k=3\). The client is able to reconstruct the public key from a subset of the key pieces. This means that not all key pieces must be obtained. Untrustworthy or topology wise far away regions can be omitted and the client can choose which regions to query for the key pieces. In that way, the key is not stored at only a single region and at least \(k\) regions must be queried to reconstruct the key.  
In comparison to distributing full key copies to several regions, this approach has an important benefit. In case a region stores the complete key and cooperates with an attacker, it could swap the key with another one. This is obviously a problem in case the requester relies only on a single region. Even if he queries multiple regions, the result will be a set of non-matching keys. This means that he needs to base his trust on the majority principle by selecting one of the keys. Some requesters might choose to cancel the connection setup because of the irregularities of the obtained key set.  
In a non-shared key system, malicious regions could generate a new public key that corresponds to their private key and use this combination to interfere the whole system. Contrary, by only holding a piece of the key, a malicious region can only toggle some bits in the corresponding part of the key. This would be detected during a request as the data queried would be invalid. Once the requester has obtained all key pieces, he only needs a subset of all pieces to reconstruct the original key. Even if he received a modified key piece, he can use various combinations of key pieces to reconstruct the public key. In case he accidentally included the modified piece, the reconstruction results in a non-valid public key format. In that way, the requester knows that at least one of the pieces was modified and he needs another variation of key pieces.
## 4 Application to Parametric Network Games
### The Linear-Quadratic Network Generalized Nash Equilibrium Problem
_If_ \(x^{*}(t)\) _is a normalized equilibrium such that the multipliers_ \((\lambda^{i*}(t),\mu^{*}_{i}(t))\) _of each player_ \(i\) _satisfy the following conditions:_ \[u_{i}(t,x^{*}_{i}(t),x^{*}_{-i}(t))\geq u_{i}(x_{i},x^{*}_{-i}(t)),\qquad\forall \ t\in[0,T],\ \forall\ x_{i}\in K_{i}(t,x^{*}_{-i}(t)). \tag{31}\]4 _for some vector_ \(\bar{\lambda}(t)\in\mathbb{R}_{+}^{m}\) _and_ \(r(t)\in\mathbb{R}_{++}^{n}\)_, then_ \(x^{*}(t)\) _is a solution of_ \(VI(F^{r(t)},K(t))\) _and_ \((\bar{\lambda}(t),r_{1}(t)\mu^{*}_{1}(t),\ldots,r_{n}(t)\mu^{*}_{n}(t))\) _are the corresponding multipliers._  
The _variational equilibria_ can then be computed by solving, for any \(t\in[0,T]\), the following variational inequality: find \(x^{*}(t)\in K(t)\) such that  
\[u_{i}(t,x^{*}_{i}(t),x^{*}_{-i}(t))\geq u_{i}(x_{i},x^{*}_{-i}(t)),\qquad\forall \ t\in[0,T],\ \forall\ x_{i}\in K_{i}(t,x^{*}_{-i}(t)). \tag{31}\]5  
As an example of network GNEP, we consider the same payoff functions defined as in (27), while the strategy set of player \(i\) is given by the usual individual constraint \(x_{i}\geq 0\) and an additional constraint, shared by all the players, on the total quantity of activities of all players, that is  
\[u_{i}(t,x^{*}_{i}(t),x^{*}_{-i}(t))\geq u_{i}(x_{i},x^{*}_{-i}(t)),\qquad\forall \ t\in[0,T],\ \forall\ x_{i}\in K_{i}(t,x^{*}_{-i}(t)). \tag{31}\]6  
where \(C:[0,T]\to\mathbb{R}\) is a given positive and Lipschitz continuous function. Depending on the specific application, the additional constraint can have the meaning of a collective budget upper bound or of a limited availability of a certain commodity. If \(\phi\rho(G)<1\), then the variational equilibrium can thus be found by solving \(VI(F,K(t))\), where \(F\) is the same as in (28) and  
\[u_{i}(t,x^{*}_{i}(t),x^{*}_{-i}(t))\geq u_{i}(x_{i},x^{*}_{-i}(t)),\qquad\forall \ t\in[0,T],\ \forall\ x_{i}\in K_{i}(t,x^{*}_{-i}(t)). \tag{31}\]7  
Notice that \(K(t)\) satisfies Assumption (3), with constant \(M\) equal to the Lipschitz constant of the function \(C\) (see [11]). Therefore, Theorems 2 and 3 guarantee that the variational equilibrium \(x^{*}(t)\) is a Lipschitz continuous function with estimated constant equal to \(\min\{\Lambda_{1},\Lambda_{2}\}\).
## 2 Acoustic Emission Test on RC Beams
### Relationship of Fracture Mechanics and Acoustic Emission Test Results  
According to the failure process of concrete, the nucleation of shear cracks follows tensile cracks on an existing crack surface [15]. Experiments with AE tests and fracture mechanics were conducted to correlate damage analysis or energy in structures [30; 31; 32]. In addition, acoustic emission and digital image correlation techniques were simultaneously used to investigate fracture parameters such as crack opening and size of fracture zone. It was found out that AE is useful in locating fracture growth due to micro- and macro-cracks, while DIC is useful to measure the crack openings. Both methods also presented a good estimation of crack tip location. It was also observed that the total number of AE events for larger beams required more energy to cause failure in beams [33].  
The FPZ is also of interest to localize damage. In a previous study, plain concrete specimens under uniaxial load were generally found to have narrower/smaller FPZs than fiber-reinforced concrete. The FPZ can be considered as an indicator of brittleness/toughness of the material. A tougher material with a higher energy capacity has a wider/larger FPZ [34].  
Fatigue crack growth in plain concrete beams using three-point loading showed that the rate of decrease in stiffness and the rate of change of crack growth was independent of the size of the specimen [35].  
Interfaces between materials also need to considered in real life situations. An investigation of a concrete-concrete interface was conducted with beams of different sizes, with a jointed interface between two different strengths of concrete. The width of the FPZ and damage zone are computed using load, displacement, AE events, and AE energy, and was found to be independent of the size. Additionally, as the difference in compressive strength of concrete on either side of interface increases, the load carrying capacity, number of AE events, AE energy, width of FPZ, and damage zone decrease [36].  
From the abovementioned discussions, modeling and DIC are mostly conducted using two-dimensional analysis. However, structures are in three dimensions and the investigation of crack opening and FPZ should be done in three dimensions as well. This paper uses convex hull volume (CHV), which is a representation of the three-dimensional analysis, to investigate the localization of damage or FPZ in the compression zone at the midspan of the beam. In relation to this, further investigations are warranted.
# A Sensor Data-Driven Decision Support System for Liquefied Petroleum Gas Suppliers
## 4 Decision Support System Workflow
### Users of the System  
There are three types of DSS users identified in Figure 2: data scientist, data analyst and services.  
The data scientist is an expert in data science who performs statistical analysis and data dependency studies and who builds the data processing models. The data scientist operates in the presented above analytical environment. Besides, this user performs the configuration of reports and dashboards. Finally, the data scientist completes the tasks ordered by another user--the data analyst. Such tasks can include, e.g., building the up-to-date predictive model generating LPG demand prognoses.  
Data analysts are domain experts. They analyze and supervise business processes performing the daily analysis of business KPIs, the analysis of reports' data, and research on identified anomalies. Data analysts use defined procedures in an environment that consists of reporting and visualization tools, as well as analytic methods developed by the data scientist. The data analyst can commission the data scientist to implement the new  
Figure 4: An illustrative example of the analytic server operation—LPG consumption prediction.  
visualization functionalities, new KPI indicators, or analytical tasks that are not available in their analytical environment.  
Service users are end users who measure and verify the service level agreement fulfillment. Two types of users belonging to this group can be indicated. The first type of service users operates on dashboards and monitors and plans daily deliveries. These delivery operators decide where and when the tankers should be directed and what the route of each tanker should be. The other type of service users makes business decisions on the basis of KPI values and alerts about high risk events and predicted total gas demand. Managers of this type operate with a medium- and long-term perspective, and their task is to prepare an appropriate stock and long-term optimization of the supply process.  
Summarizing the presented user types: the data scientist user works on the DSS site developing its analytical functionality; the data analyst user is on the edge of the system and its application domain; while the service users utilize the DSS in daily operation and make strategic business decisions.
# Influence of Climate Conditions on Deficiencies of Building Roofs
## 3 Results
### Analysis of Climate-Geographical Conditions
#### 3.4.2 Moisture by Infiltration (TCD2)  
This deficiency is more frequent in flat roofs (FR = 75.29%) than in pitched roofs (PR = 24.71%). It was found to be more present in the Mediterranean (41.44%) climate than in the Continental (30.80%), Oceanic (24.71%) or Sub-tropical (3.04%) climates. It occurs more frequently in the North latitude (53.23%) than in the Central (33.08%) or South (13.69%) latitudes, although it appears less frequently Inland (32.70%) than in Coastal (67.30%) zones. It was also found that this deficiency is more frequent in zones with Medium (56.65%) precipitation than in zones with High (24.71%) or Low (18.64%) precipitation.  
The causes of greater moisture by infiltration in flat roofs are similar to those indicated in the previous point.
# Automatic Modulation Classification Using Hybrid Data Augmentation and Lightweight Neural Network
## 3 Our Proposed AMC Method
### Details of the Lightweight CVResNet
#### 3.2.2 Complex Convolution  
Regular neural networks are defined in the real-valued domain and their weight parameters and the data passed in the network are real-valued numbers. The complex-valued neural network can be considered an extension of the conventional neural network from the real-valued domain to the complex-valued domain, where the network parameters are all complex-valued numbers and allow the input of the network to be in the form of complex-valued numbers. Therefore, it can be used to process modulated signals in IQ format and better retain the information contained in the modulated signals.  
To reduce the complexity caused by complex-valued neural networks, only complex convolution [55] is used in this paper. As shown in Figure 4, the idea is to treat the real and imaginary parts of the complex numbers as logically distinct real-valued subjects and to simulate complex operations internally with real-valued arithmetic. By defining the input complex-valued data \(\mathbf{S}=\mathbf{I}+i\mathbf{Q}\) and the convolution kernel \(\mathbf{K}=\mathbf{a}+i\mathbf{b}\), where \(\mathbf{a}\) and \(\mathbf{b}\) are independent real convolution kernels, the convolution result of \(\mathbf{S}\) with \(\mathbf{K}\) can be expressed as  
\[\mathbf{S}*\mathbf{K}=(\mathbf{I}*\mathbf{a}-\mathbf{Q}*\mathbf{b})+i(\mathbf{ Q}*\mathbf{a}+\mathbf{I}*\mathbf{b}), \tag{5}\]  
where \(*\) is the convolution operation. From this formula, it can be seen that complex convolution is the conversion of the convolution of complex numbers with complex numbers into the convolution of real numbers with real numbers, and combining them, so that complex convolution can be achieved without modifying the real-valued convolution operation.  
Figure 4: Complex convolution.  
Figure 3: The structure of ResNet.
# Merging Ontologies and Data from Electronic Health Records
## 3 Background  
### Electronic Health Record  
Starting from an academic idea in the late 1980s, EHRs have evolved to become the centre of national health information strategies in most European countries [26], including Italy, and also outside Europe. The term EHR was officially introduced in Italy in the national legislation of Article 12 of Law 221/2012 [27].  
In the real world, the potential of digital medical records even today is often not yet fully understood nor are the benefits for citizens (who can have a complete overview of their medical history at hand), clinicians (who manage to organise themselves more efficiently), and medical centres/clinics (which obtain both economic and time savings in the management of clinical work). For better sharing data between all the involved partners in the medical sector, i.e., patients, doctors, nurses and other involved professionals at any level, standards need to be met in order to minimise the effort.  
The major standards for representing EHRs are as follows [28].  
1. OpenEHR: a framework of standards for health care and medical research. Its mission is to facilitate the creation and sharing of health records by patients to clinicians via open-source, standards-based implementations [29].
2. HL7: Founded in 1987, HL7 is an ANSI accredited standards development non-profit organisation dedicated to providing a comprehensive framework and related standards for exchange, integration, sharing, and retrieval of electronic health information that supports clinical practice and the management, delivery, and evaluation of health services. HL7 is supported by over 1600 members from over 50 countries, including over 500 corporate members representing healthcare professionals, government stakeholders, taxpayers, pharmaceutical companies, vendors/suppliers, and consulting firms.
3. CEN TC251 EN 13606: standardisation in the field of health information and communication technologies (ICT) to achieve compatibility and interoperability between independent systems and allow modularity [30]. This includes health information structure requirements to support clinical and administrative procedures and technical methods to support interoperable systems; as well as safety, security and quality requirements.
## 5 Evaluation of STAR COSMIC-2 Wet Profile Retrievals through Comparison with CrIS and ATMS
### Comparisons with CrIS Measurements Using CRTM and Double-Difference Method  
#### 5.1.1 Validation Approaches  
This section highlights COSMIC-2 multi-sensor calibration and validation studies conducted in STAR (see Figure 1). Here we evaluate COSMIC-2 STAR temperature and humidity retrievals (WetPrf) by comparing them with CrIS measurements using the community radiative transfer model (CRTM) [45; 46; 47]. CRTM is a fast radiative transfer model for calculations of radiances for satellite IR or MW radiometers such as CrIS and ATMS and outputting IR/MW radiance and brightness temperatures at CrIS/ATMS's spectral resolutions. In the section, the STAR COSMIC-2 1D-Var temperature and humidity profiles are used as inputs for the CRTM for radiance/brightness temperature simulation (also see [36]).