esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Propagation Model
### Model Requirements  
Our technique illustrates the photon transmission as a probability distribution that describes the path length of the photon propagation before a photon interacts with a particle and the angles of scattering after a scattering event occurs. Since our model incorporates the Monte Carlo approach, which is statistical in nature, it relies on calculating the radial angles and directions of a large number of photons (usually \(10^{5}\)). Our method tracks photon propagation through water by repeatedly and randomly choosing a random number originator to generate bits in the interval \((0,1)\) and random angles between \((0\) and \(2\pi)\). However, in underwater communications, due to limited knowledge about the transmission, reception, and channel conditions, we have made certain assumptions as follows:  
* no shift in frequencies of the incident and scattered light is considered, the only change that is considered is the change in direction;
* the phase functions are used to measure the anisotropy of scattering;
* scattering and absorption are uniform throughout the channel;
* the boundaries are absolutely absorbing, and any photon traveling past the receiver or transmitter plane is marked as terminated.  
Our simulation model comprises the following three stages  
1. the photon's initial conditions,
2. light transport under water, and
3. photon reception.
# Nonlinear Dynamics of a Resonant-Impact Dielectric Elastomer Actuator
## 3 Numerical and Experimental Study Results
### Effects of Excitation Voltage Amplitude  
In this subsection, the effects of the excitation voltage amplitude on the performance of the resonant-impact DEA system are investigated both numerically and experimentally. In the nondimensional numerical simulation, the voltage amplitude \(\phi_{p\cdot p}\) is varied from 0.1 to 0.5 in 80 steps and the frequency responses from \(\omega_{e}\) = 0.01 to 4 in each voltage case are simulated. The oscillation amplitudes of the system (\(\left|amp.\right|=\left|x_{max}-x_{min}\right|/2\), where \(x_{max}\) and \(x_{min}\) are the maximum and minimum displacement at steady state, respectively) against the excitation voltage frequency \(\omega_{e}\) and amplitude \(\phi_{p\cdot p}\) are plotted in Figure 7a. It can be noted that \(\left|amp.\right|\) increases with the increasing voltage amplitude due to the higher input energy, while the frequency band with high-amplitude oscillations (the red region in Figure 7a) widens as well. The relationship between the peak frequency \(\omega_{p}\) (the frequency that yields the highest oscillation amplitude/impact force and maintains a single stable response) and peak impact force \(\left|f_{max}\right|\) (the impact force amplitude at \(\omega_{p}\)) against the voltage amplitude are plotted in Figure 7b. Note that no impact is triggered in this system when \(\phi_{p\cdot p}\) is small due to insufficient oscillation amplitude. As \(\phi_{p\cdot p}\) increases to \(\sim\)0.19, impacts occur and \(\omega_{p}\) and \(\left|f_{max}\right|\) continue to increase with increasing \(\phi_{p\cdot p}\).  
In addition, the effects of excitation voltage amplitude on the resonant-impact DEA system were investigated in experiments with excitation voltage amplitudes \(\Phi_{p\cdot p}\) of 1.7, 2.1, 2.5, 2.9, 3.3, and 3.7 kV. Numerical simulations were conducted using the DEA design parameters listed in Section 2.3.1 and the modelling parameters from Section 2.3.2. The measured and corresponding modelling results for the peak frequency \(\Omega_{p}\) and peak oscillation amplitude \(\left|Amp.\right|\) (defined as the oscillation amplitude at frequency \(\Omega_{p}\)) are shown in Figure 8a. It can be seen that both \(\Omega_{p}\) and \(\left|Amp.\right|\) increase slightly with the \(\Phi_{p\cdot p}\) from  
Figure 7: **(a)** Effects of excitation voltage amplitude on the frequency responses of the resonant-impact DEA system and (b) effects of excitation voltage amplitude on the peak frequency and peak impact force.  
1.6 to 3.7 kV. On the other hand, note from Figure 8b that the peak impact force \(\mid\)F\({}_{max}\)\(\mid\) increases approximately linearly with the increasing voltage amplitude from \(\sim\)0.2 N at \(\Phi_{p\_p}\) = 1.6 kV to over 1.3 N at 3.7 kV. All of these findings agree well with the results of the nondimensional studies shown in Figure 7.
## 3 Results
### Polyphenols and Phenolic Acids in Artemisia Species  
LC-MS analysis of sample extracts of _Artemisia dracunculus_ L. (ADr), _Artemisia annua_ L. (AAn) and _Artemisia absinthium_ L. (AAb) were conducted under identical solution and instrumental conditions. The obtained results revealed the identification and in some cases quantification, according to their \(R_{\text{t}}\) and \(m/z\) values, of a total of 11 polyphenols in all analyzed extracts combined with some differences in terms of their expression in each extract (Table 2). Identified polyphenols fall into polyphenolic acids group, cinnamic acid derivatives, and flavonoids, flavones and flavonoids respectively. Identified phytocompounds were gentisic acid, chlorogenic acid, caffeic acid, ferulic acid, isoquercitrin, rutin, quercetin, quercetin, luteolin, kaempfer and apigenin, expressed as \(\upmu\)g/mg d.e. The most important polyphenols identified in all extracts, consistent with their concentration, were chlorogenic acid, rutin and quercetin, the most abundant one being chlorogenic acid. Meanwhile, gentistic acid, caffeic acid, ferulic acid, isoquercitrin, quercitrin, luteolin, and apigenin were identified in smaller concentrations, or even in traces, falling below the limit of quantification in some cases. Some differences regarding the expression of polyphenols in the three types of extracts were spotted out, such as quercetin and isoquercitrin, identified in AAb and AAn extract, but not in ADr extract, concurrently, kaempfer, which was found only in ADr extract, in a substantial amount. The expression of the dominant compound, chlorogenic acid respectively, was almost equivalent in AAn and ADr extracts, while AAb exhibited a concentration almost four times lower. The richest extract consistent with the concentration of identified compounds was ADr, while AAb contained the lowest amounts.
# A Real-Time On-Orbit Signal Tracking Algorithm for GNSS Surface Observations
## 4 Method, Part 2: Open Loop GNSS-R Delay and Doppler Tracking Algorithms
### Instrument Hardware Considerations  
A key requirement in the tracking algorithm presented here is that signals from the two divergent paths from the transmitter to the receiver (Figure 1) must be clocked into the GNSS-R instrument with a common clock. If this is not the case, calculating the absolute code phase reference for both signals must consider the precise sub-millisecond timing and transmitter and receiver clock biases, as was outlined in [25]. However, if the zenith navigation and reflected nadir signals are both clocked into the digital signal processor with a common clock, then the sub-millisecond sampling reference is the same and the problem of predicting the reflected signals code phase time delay reduces to a relative calculation with respect to the code phase of the directly tracked signal from the same GPS transmitter, which is used by the navigation portion of the receiver for position and velocity estimation. A common clock configuration for a GNSS-R instrument is illustrated in Figure 2.
# Reconstruction of the Physiological Behavior of Real and Synthetic Vessels in Controlled Conditions
## 2 Materials and Methods  
### Experimental Set-Up  
An Artificial Circulatory Phantom (ACP) (Figure 1), designed to simulate the functioning of a segment of the human cardiovascular system, was constructed explicitly for this project and comprehensively detailed in our previous work [22]. In essence, at the core of the setup, a transparent rectangular vessel reactor, constructed from Plexiglas and equipped to maintain a constant temperature (temperature was measured with an installed temperature sensors), was positioned. The reactor was filled with a non-commercial fluid designed to mimic the rheological properties of blood. This fluid comprised 60% distilled water and 40% glycerol, presenting a density of 1.2 g cm\({}^{-3}\) and a viscosity of \(4.8\times 10^{-3}\) Pa\(\cdot\)s. The fluid was supplied from a fluid chamber via a pump. The pulsating character of the flow was achieved by self-made artificial heart. This device manifested as a moving plunger housed in a stainless-steel corpus and driven by an electric motor, controlled discharge pressure, discharge volume, and the pulsation of prediction. These parameters were controlled by an electrical engine connected to the computer. Each time, a specified amount of the medium supplies the analyzed vessels, as well as ePTFE vascular prosthesis.  
The vision system (VS) incorporated nine cameras positioned on an aluminum circular profile parallel to the floor, enabling horizontal movement [19]. The size of a pixel in the captured images of the real object was 0.35 mm. To increase the accuracy of vision acquisition, cameras were able to move 10 deg. Steps were included to both sides in the range of 40 deg. The entire system was controlled by a portable workstation (Dell Precision M6400 (Dell, Round Rock, TX, USA)) equipped with a four-core Intel CPU (2.4 GHz), 4 GB RAM (1333 MHz), and a 500 GB SSD HD. A self-developed acquisition and control software (ACS) facilitated the operation of the system.  
Figure 1: An artificial circulatory phantom (ACP). The system comprised the following: 1—an artificial heart; 2—a transparent, rectangular chamber for the vessel; 3—a transparent container with distilled water and glycerol; 4—temperature sensors; 5—a camera; 6—a pump for distilled water and glycerol; 7—computer.
# Triangular Position Multi-Bolt Layout Structure Optimization
## 3 Results
### Optimized Stress Comparison between Front and Rear Bolts
Before and after optimization, the maximum stress of the nickel steel plate is 311.5 MPa and 302.8 MPa, respectively, and the maximum stress is reduced by 8.7 MPa, with an optimization rate of 2.8%. The maximum stress at bolt hole B decreased from 307.1 MPa to 177.5 MPa, and the optimization rate was 42.2%, which significantly improved the stress concentration phenomenon at bolt hole B. The maximum stress at bolt holes A and C decreased less, 8.9 MPa and 6.4 MPa, respectively, but the equivalent stress around them decreased significantly. According to the  
can provide an effective reference for engineers when designing multi-bolt connection structures for nickel steel flat plates. Before and after the optimization, the stress concentration around the hole of the nickel steel plate is improved, especially the maximum stress at the bolt hole B, which reaches 40-60% of the optimized value. The overall optimization rate for the upper nickel steel plate is 24% and the overall optimization rate for the lower nickel steel plate is 2.8%, mainly due to the fixed restraint applied on the left side. The overall optimization rate for the bolt set was 12.5%. In order to study the optimization under different force conditions, this paper changed the pressure and tension and added comparative analysis, respectively, 3 KN, 9 KN, 15 KN and 21 KN, as shown in Figure 13. The results show that under the 18 KN load, the nickel steel plate and bolt set above can achieve better optimization efficiency, and their optimization rates are above 10%. At 15 KN, the optimization effect is better. However, when the load is greater than 18 KN, the optimization efficiency is lower. When the load is greater than 21 KN, the nickel steel plate has exceeded the yield limit and presents a large deformation condition, which is not suitable for calculating its optimization efficiency. The optimization efficiency of the lower nickel steel plate is always lower, mainly because of the fixed constraints on the lower nickel steel plate. Overall, the analysis of this paper proves that the gray wolf algorithm is suitable for structural optimization of multi-bolt layouts in triangular positions, which helps to reduce the stress concentration phenomenon and contributes to the effective structural optimization design of large and complex equipment and aeronautical structures.
# A Complete Key Management Scheme for LoRaWAN v1.1 +
## 4 LoRaWAN v1.1 Security Structure Analysis
### The Potential Security Vulnerabilities of LoRaWAN v1.1 Key Management Scheme  
LoRaWAN v1.1 has presented the key derivation and distribution scheme and has ensured both confidentially of the network and application payloads as well as the integrity of the payload messages, the secret root keys are used for authentication of the end device. In this section, vulnerabilities and potential security risks of LoRaWAN v1.1 key management scheme are discussed.  
In the LoRaWAN v1.1 specification, the key generation scheme is vague and outlines only general security requirements. A secure and efficient key generator procedure is essential to ensure a secured system that is hard to breakthrough. Since secret keys are essential to the overall security of LoRaWAN, key independence ensuring that one key procedure does not infer, another key must be implemented. Thus, a high level of randomness in the key generation is needed. Low randomness in the key generation poses a danger that some traits of one key can enable a cryptanalytic deduction of other keys [27]. Moreover, a key generator needs to be computationally efficient. The current version of LoRaWAN includes the AES algorithm, which is used for encryption and decryption. Hence, the current key generator appears to be computationally heavy for an efficient key generation. A solution improving both the key randomness and computational efficiency is proposed in Section 6 of this paper.  
LoRaWAN has introduced new updating methods for session keys by using the Rejoining-Request message, which is always initiated by the end device. However, the recommended frequency of the session key updating scheme is not specified in detail. Therefore, questions like how often and under what circumstances should the key be updated or under what situation should the key be updated need to be answered. A high key-updating frequency at a low computational cost would lead to an increased network security [28]. In addition, the high frequency of key updating should not compromise the energy cost of the device too much. Otherwise, it would affect the lifetime of the device deployment.  
As join servers hold the root keys of the end devices. The root keys are fundamental to the overall security of the network communication. How does the LoRaWAN system can make sure the communication is still running smoothly in the case of a join server being down? The answer to this question is in a backup system, which can provide the key backup and recovery.  
Backward compatibility would result in a lower security grade. The LoRaWAN v1.1 supports the backward compatibility, it means a device can be connected to a network server with a lower LoRaWAN version number. However, it introduces vulnerabilities that are associated with the older versions [22; 23].
# Credibility Based Imbalance Boosting Method for Software Defect Proneness Prediction
## 2 Related Work
### Class-Imbalance Learning
#### 2.2.2 Algorithm-Level Methods  
Algorithm level methods modify the classification algorithm to alleviate the effect of class-imbalance on the minority class samples, which include cost-sensitive methods [70; 71], ensemble methods [46; 72], and hybrid methods, such as cost-sensitive boosting methods [45; 73] and oversampling boosting methods [74; 75].  
Cost-sensitive methods take the misclassification cost into consideration when building prediction model and try to develop a classifier that obtains the lowest cost. Before using cost-sensitive learning methods, users must define a cost matrix. Let \(C(i,j)\) denote the cost of classifying a sample from class \(i\) to \(j\). In the binary classification task, \(C(0,1)\) represents the cost of misclassifying a negative sample as a positive sample and \(C(1,0)\) represents the cost of misclassifying a positive sample as a negative sample. In software defect proneness prediction, cost-sensitive learning methods assign a larger cost of false negative samples (i.e., \(C(1,0)\)) than that of false positive samples (\(C(0,1)\)), hence resulting in a performance improvement on the minority (aka. positive) class samples.  
Ensemble methods refer to combining multiple base learners (such as decision tree, logistic regression, and support vector machine) in order to construct a strong model. There are two famous ensemble learning methods: bagging framework based random forests [76] and boosting framework based AdaBoost [46]. AdaBoost is an effective ensemble method that was proposed by [PERSON] and [PERSON]. It combines a series of base classifiers to be a stronger classifier by several learning iterations. In each iteration, misclassified samples are given higher weights to make the next new classifier to focus on these samples. The learning process tries to minimize the classification cost of all the samples.  
Some algorithm-level methods combine the boosting algorithm and cost-sensitive algorithm, such as AdaC2 [45] and AdaCost (Cost-Sensitive Boosting) [73]. Meanwhile, some methods take the advantages of both boosting and cost-sensitive algorithms, e.g., RAMOBoost (Ranked Minority Oversampling in Boosting) [74]. RAMOBoost assigns weights to misclassified minority class samples that are based on the number of neighbor majority class samples and selects those misclassified minority class samples with higher weights in order to generate synthetic samples.
## 4 Results and Discussion  
The model described in the previous section has been tested in a real scenario within the WoO project framework. For the purpose of this article, the use case evaluated here corresponds to an open smart neighborhood where the WSN is deployed in a house to provide the proper home automation through web services.  
The sensors used during the tests were SunSPOT programmable motes. These sensors run on the ARM architecture, and have an integrated radio support in the 2.4 GHz band using IEEE 802.15.4. They are also equipped with a sensor board capable of providing temperature, luminosity and acceleration readings, along with the possibility of being interfaced with external sensors.  
Indeed, one sensor used in these tests was a LV-MaxSonar-EZ0 used as a proximity sensor to detect possible undesired presences in the house. This sensor was virtually represented in the gateway.  
Another sensor was programmed to virtualize a heat detector used for fire detection. In this case, the event service was composed using the temperature service offered by other sensors deployed in the network. Thereby this sensor can reuse other temperature services already deployed in any network to create a new event source for fire detection taking as a reference the operational specifications recommended by the European Normative EN 54-5:2000 [16].  
In the test scenario there were three basic temperature sensors, the proximity sensor and the fire sensor, which also provided a mean temperature and was planned to serve as an in-network alarm dispatcher. Regarding the gateway, it was running on an Asus EB1033 PC using Ubuntu 12.04 LTS. The computer was connected to the WSN using a SunSPOT base station--a basic SunSPOT device without any kind of sensor board or battery--, using the abovementioned IEEE 802.15.4 radio interface. The gateway itself was a service integrated in an Enterprise Service Bus (ESB) platform using Fuse ESB Enterprise 7.1.  
There are three main components in the gateway service. The first one is related to the nSOM middleware. It is in charge of discovering the services exposed by the devices in the WSN, and creating a virtual representation of them by exposing the access to their resources as REST services. It is also in charge of translating any message received from the WSN to the corresponding REST service, and reversely translating any REST invocation to the proper WSN message.  
The second one is a registry that dynamically stores all the related information to the services discovered in the WSN, including the events sources and consumers. The registry was implemented using a MySQL 5.5 database server.  
Last but not least, there is an event manager which is responsible for handling the events detected in the WSN and dispatching them to the list of registered consumers using the REST approach described in the proposal. The whole system has been integrated with other WoO partners' contributions to the open smart neighborhood scenario.
# Comparing Monitoring Networks to Assess Urban Heat Islands in Smart Cities
## 2 Materials and Methods
### Materials  
This research is based on the study of the RTD networks for measuring urban temperatures in Caceres, a medium-sized city in western Spain of around 96,000 inhabitants [52] and a surface area of around 2300 hectares, over a typical period of extreme heat between 1 and 26 August 2024.  
On the one hand, a bespoke network was studied. This is made up of 25 AWSs and was deployed in July 2024 by researchers from the Polytechnic School of Caceres as part of the project Heat Waves and Cities: Adaptation and Resilience of the Built Environment [53]. One of the principal specific objectives was the digital modeling of the city through low-cost, open-source, and continuous monitoring.  
On the other hand, two CWON were analyzed. These are collaborative systems through which private users or organizations (such as educational institutions, meteorological organizations, and other entities) can install and use commercial AWS, usually low cost, to generate a climate database accessible to both researchers and the general public [34]. In this study, the two CWONs selected have the greatest number of devices deployed around the city. The two networks are those of the commercial enterprise Netatmo, with 14 AWSs in the city. This enterprise specializes in the creation of intelligent devices for the home and their integration into their own system [54; 55]. The second network belongs to the Weather Underground network platform [56], with 10 AWSs in the city from different manufacturers and of differing types. This network provides data concerning hyperlocal air quality and other meteorological data through the information from CWSs. Other CWON, such as Meteoclimatic [57], Noromet [58], and NCEI [59], were discarded due to their low or null presence in the city.  
The density of the measuring devices of these networks in the city oscillates between approximately three units and one unit per 200 Ha, respectively. This supposes homogeneously distributed squares or grids measuring between 800 by 1400 m.