esa-sceva/satcom-chunk-collection huggingface dataset:
# Security Architecture for Swarms of Autonomous Vehicles in Smart Farming
## 2 Materials and Methods
### State of the Art  
#### 2.2.1 Security Architecture Goals  
Security architecture refers to the processes and tools used to prevent or mitigate attacks on a particular system. The primary purpose of the security architecture presented by the authors of this paper is to protect from cyber harm, solutions or systems for managing heterogeneous swarms of autonomous vehicles. This design is based on the security architecture of the OSI Reference Model (ISO 7798-2), which covers secure communications between open systems. This part of ISO 7798 considers that, to maintain the security of transmissions in a communication network, the following aspects must be guaranteed:  
* Confidentiality, to protect the data against non-authorised revelations, ensuring that only authorised people are allowed to access the information;
* Integrity, to protect the data against non-authorised modifications, insertions or deletions;
* Authentication, to verify the supposed identity of a user or a system;
* Access control, to protect the system resources against non-authorised users; and
* Non-repudiation, to prevent an entity from denying or refuting previous commitments, responsibilities or actions.  
Furthermore, correct system operation (security of process) must also be ensured by implementing measures that guarantee system recovery and backup in case of software failure, incorrect configuration or damage by malware.  
The security architecture presented in this paper covers all of these aspects related to communication security, proposing and evaluating different alternatives for each of the aforementioned aspects and giving recommendations on how to apply them in a practical way to the target system depicted in Figure 1. This security architecture is described in detail in Section 3.2.
## 2 Background and Related Works
### Long-Distance QKD  
Extending QKD over long distances can be achieved using a hop-by-hop approach, employing trusted relays between distant nodes to mitigate distance limitations [21]. In this scenario, intermediate nodes establish secure keys with their adjacent nodes, effectively creating a chain of secure links that spans the desired distance.  
For instance, consider a network where nodes \(B\) and \(C\) act as relays between nodes \(A\) and \(D\), as illustrated in Figure 1. Node \(A\) establishes a secret key \(K_{1}\) with node \(B\), and node \(B\) establishes another key \(K_{2}\) with node \(C\). Through a process of key forwarding and re-encryption, node \(C\) eventually shares a key with node \(D\), allowing \(A\) and \(D\) to share a global key.  
This method relies on the assumption that all intermediate nodes are fully trusted. However, the security of such networks is highly dependent on the integrity of each relay node. If an adversary compromises any relay, the overall security of the key distribution process is jeopardized. To address this risk, alternative approaches, such as QKD networks with partially trusted relays and the development of quantum repeaters, have been proposed to enhance security and extend communication distances without relying solely on trusted nodes [22].
## 2 Background and Related Work
### Cache Attacks
#### 2.2.2 Flush+Reload  
This technique relies on the existence of shared memory: thus, when an attacker flushes the desired lines from the cache, he can be sure that, if the victim process needs to retrieve the flushed line, it will have to load it from main memory. _Flush+Reload_ also works in three stages: First, the desired lines from the cache are flushed (reverse engineering may be required to determine the addresses that can leak information). Second, the victim runs its process or a fragment of it. Third, the flushed line is accessed by the attacker, measuring the time required to do it. Depending on the reload time, the attacker decides whether the line was accessed.  
This attack uses the clflush instruction to remove the target lines from the cache in the initialization stage. It is easy to implement and quite resistant to micro-architectural noise, thus it has become popular. However, its main drawback is that it requires memory deduplication tobe enabled. Deduplication is an optimization technique designed to improve memory utilization by merging duplicate memory pages. Consequently, it can only recover information coming from statically allocated data. Shared memory also implies that the attacker and the victim have to be using the same cryptographic library. To eliminate the need of knowing the version of the algorithm attacked, [PERSON] et al. presented the cache template attack [34], where they enforced the existence of shared memory between the attacking and the attacked processes.  
The attack was first introduced by [PERSON] et al. [33], and later extended to target the LLC to retrieve cryptographic keys, TLS protocol session messages or keyboard keystrokes across VMs [2; 34; 35]. It has also demonstrated its power against AES T-table based implementations [36], RSA implementations [2], or ECDSA [37], among others. It can also detect cryptographic libraries [38]. Further, [PERSON] et al. [39] showed that it is applicable in several commercial PaaS clouds, where it is possible to achieve co-residency with a victim [13].  
Relying on the clflush instruction and with the same requirements as _Flush+Reload_, [PERSON] et al. [3] proposed the _Flush+Flush_ attack. It was intended to be stealthy and bypass attack monitoring systems [19; 20]. The main difference with _Flush+Reload_ is that this variant recovers the information by measuring the execution time of the clflush instruction instead of the reload time, thus avoiding direct cache accesses. This was the key fact to avoid detection. However, recent works have demonstrated that it is detectable [21; 40].
## 4 Communication Architecture
### Network Energy Consumption Model
The CH consumes 0.33% of its energy for a transmission time interval of 10 s and 0.31% for 30 s, the level 2 sensors consume 0.30% for 10 s and 0.29% for 30 s. Level 1 sensors consume the least amount of energy since 0.24% of energy is consumed for a transmission time interval of 10 s and 0.08% for 30 s. This is due to the fact that level 1 sensors wake up periodically to transfer the collected data to their CH. Confidence intervals in the simulations from Omnet/INET are less than 94.9%. The results of the various simulations show that the data transmission period of the sensors is a significant factor in optimizing the service life of LS-WSNs. This consumption decreases as the data transfer time interval increases, as shown in Figure 16.  
Subsequently, the impact of the network density on the life of the network was studied. Figure 17 shows that the energy consumption of the sensors on the second level of the architecture is dependent on the number of sensors deployed. It is easy to notice through our simulations that the energy consumption of the sensors of this level is proportional to the deployed sensors. These different results show that in addition to the three-tier architecture for LS-WSN deployment, the management of data transmission periods and sensor density contribute well to extending the service life of LS-WSNs. Figure 15: Average energy consumption of each group of sensors in the network. Finally, we evaluated the impact of modeling the tasks of the different sensors across the three levels of the architecture on the energy consumption of the LS-WSN. To do so, we compared our sensor arrangement model with two other models, namely the \"Mannasim\" model with a single sensor level implemented in the \"Homogeneous network\" application and the \"multi-level\" model (MVSN). The different figure highlights the impact of scaling on the average energy consumption of the network. This consumption decreases as the surface area increases (here 15 000 level 2 sensors, transmission interval: 10 s). Figure 16: Average power consumption of the level 1 sensor as a function of the transmission interval. Figure 10:0 Energy consumption of level 2 sensors according to their number in the network. We note that the wake-up mechanism that we have implemented in our model through CH, level 2 and Sink sensors allows us to significantly reduce energy consumption by up to two compared to the \"single-sensor level\" model. However, in the same scenario, our model consumes about 1.7 times more energy than the MVSN network. This result can be explained by the continuous detection implemented in the behavior of the level 1 sensors. Therefore, adjusting the sensing interval could optimize performance. These results give indications on the impact of the different parameters on the performance of our network, namely: the impact of the nature of the sensors (use of components and energy resources of the sensor groups), the impact of the sensor unit, the impact of the sensor transmission interval (N, N2) and the impact of the scaling.
Entropy_**2013**, _15_, 1503-1515; doi:10.3390/e15051503  
_Entropy_**ISSN 1099-4300**  
www.mdpi.com/journal/entropy  
_Article_  
**Time Reversibility, Correlation Decay and the Steady State Fluctuation Relation for Dissipation**  
**[PERSON] \({}^{1,2,\ast}\), [PERSON] \({}^{2}\), [PERSON] \({}^{3}\) and [PERSON] \({}^{4,5}\)**  
\({}^{1}\) Australian Institute of Bioengineering and Nanotechnology and School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Qld 4072, Australia  
\({}^{2}\) Queensland Micro-and Nanotechnology Centre and School of Biomolecular and Physical Sciences, Griffith University, Brisbane, Qld 4111, Australia; E-Mail: [EMAIL_ADDRESS]  
\({}^{3}\) Research School of Chemistry, Australian National University, Canberra, ACT 0200, Australia; E-Mail: [EMAIL_ADDRESS]  
\({}^{4}\) Dipartimento di Scienze Matematiche, Politecnico di Torino, Torino 10129, Italy; E-Mail: [EMAIL_ADDRESS]  
\({}^{5}\) INFN, Sezione di Torino, Via P. Giuria 1, Torino 10125, Italy  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +61-7-3346-3939; Fax: +61-7-3346-3992.  
_Received: 4 March 2013; in revised form: 17 April 2013 / Accepted: 19 April 2013 / Published: 25 April 2013_  
**Abstract:** Steady state fluctuation relations for nonequilibrium systems are under intense investigation because of their important practical implications in nanotechnology and biology. However the precise conditions under which they hold need clarification. Using the dissipation function, which is related to the entropy production of linear irreversible thermodynamics, we show time reversibility, ergodic consistency and a recently introduced form of correlation decay, called T-mixing, are sufficient conditions for steady state fluctuation relations to hold. Our results are not restricted to a particular model and show that the steady state fluctuation relation for the dissipation function holds near or far from equilibrium subject to these conditions. The dissipation function thus plays a comparable role in nonequilibrium systems to thermodynamic potentials in equilibrium systems.  
**Keywords:** steady state fluctuations; dissipation; nonequilibrium molecular dynamics simulation; entropy production  
**Classification: PACS** 05.40.-a,05.70.Ln,05.20.-y
# May the Extensive Farming System of Small Ruminants Be Smart?
## 3 Results
### Non-Wearable Sensors and Network Technologies
In the IoT system, collars constitute the main data collection interface, responsible for supervising behaviour and location. The posture control algorithm is executed locally, analysing and applying corrective stimuli (e.g., electrostatic and auditory signals). Subsequently, user-relevant data are transmitted to an infrastructure network of fixed beacons [47]. These devices, installed according to the intended grazing areas, implement periodic and synchronised signal emission throughout the network, allowing the collars to evaluate their position through RSSI-based localisation techniques. The Gateway is an aggregator element that communicates with all beacons and connects to the local internet network via broadband. The data are collected on data structures without the internet and sent to a cloud platform. As reported by [PERSON] et al. [52], this project is one of the first developed in a functional way for the sheep species, collecting information regarding the sheep's posture, location, and infractions. The main non-wearable devices and applications in sheep extensive farming systems are shown in Table 2.  
Recent PLF solutions provide essential information that supports farmers' decision-making to monitor the welfare of small ruminants at a pasture. The technologies presented include non-wearable and new sensors, highlighting that they all have different strengths and weaknesses, especially when applied to the pasture-based sheep farming system (Table 3). Indeed, some systems provide excellent utility for grazing animals by providing real-time information on location, behaviour, health, and social status. However, most of the devices available for sheep have not yet been validated, therefore providing less robust and accurate data. For other systems, the purchase cost is too high, or the farms are located in areas without internet connection. Advances in computer science, electronic engineering, and artificial intelligence are the bases for efficiently and automatically monitoring animal activities and welfare.
# Application of Improved LightGBM Model in Blood Glucose Prediction
## Materials and Methods
### Improved LightGBM Model Based on Bayesian Hyper-Parameter Optimization Algorithm  
The experiment was conducted on a computer with Intel I5 8400 2.8 GHz six-core six-thread central processing unit (CPU), 16G random-access memory (RAM), and Windows 10 operating system. The simulation platform is Pycharm, and Python was used for programming, with sklearn, pandas, and numpy libraries adopted.  
Because LightGBM has many parameters, the manual adjustment of parameters will be complicated, and considering its parameters have a great impact on experimental results, it is particularly necessary to use Bayesian hyper-parameter optimization algorithm for parameter optimization. The Hyperopt used in this paper is one of the Bayesian optimization libraries in Python. It is also a class library used in distributed asynchronous algorithm configuration in Python, with a faster speed and better effect in finding the optimal parameters of the model than the traditional parameter optimization algorithms.  
The specific steps are as follows:  
1. Divide the dataset into training set and test set, process the missing values, analyze the weight of the influence of the eigenvalues on the results, delete useless eigenvalues, and delete outliers;
2. Use the Bayesian hyper-parameter optimization algorithm for the parameter optimization of the LightGBM model, and the HY_LightGBM model is constructed and trained;
3. Use the HY_LightGBM model for prediction and output the prediction results.
4. The specific experimental process is shown in Figure 5.
# Impacts of Land-Use Change from Primary Forest to Farmland on the Storage of Soil Organic Carbon
## 4 Discussion
### Dynamic Effects of the Growth Environment on SOC  
Land tillage can rapidly impact SOC, highlighting the need for appropriate agricultural management to enhance soil carbon sequestration capacity. Tillage affects not only the total carbon pool but also the microbial carbon pool, which can alter soil fauna and metabolic rates [29]. To increase SOC storage and SOM accumulation, management measures such as returning farmland to forests, nutrition management, irrigation management, grazing management, rational use of cover crops and grasses, and introducing beneficial soil organisms such as earthworms can be employed. Secondly, nutrient management and irrigation management can adjust the soil nutrient status and water supply, which is beneficial for crop growth and the accumulation of organic matter. At the same time, grazing management can improve the ecosystem function of grasslands and promote the accumulation of soil organic matter through appropriate grazing intensity and rotational grazing system. Additionally, proper utilization of cover crops and grasslands can increase the input of organic matter to the soil surface and protect the soil from erosion. Finally, introducing beneficial soil organisms such as earthworms and other soil microbes can improve soil structure and aeration, promoting the decomposition and transformation process of soil organic matter.
# A Novel RBFNN-CNN Model for Speaker Identification in Stressful Talking Environments
## 4 Model Architecture
### Traditional Classifier Experiments  
In this section, we discuss the traditional machine learning algorithms that were evaluated for speaker identification using the Emirati speech database and the global  
Figure 4: The block diagram of the proposed cascaded model (RBFNN-CNN) under stressful talking conditions.  
Figure 3: CNN model.  
English SUSAS database in stressful talking environments. In this study, we used the KNN, SVM and MLP classifiers. Within each model, there is one classifier, where 80% of utterance for the individual speaker has been used for training. Testing and evaluation include 20% of utterances.
## 3 Experimental Assessment of Mixed Metal Oxide Systems for TCES
### Mn-Cu-O Mixed Oxide System  
Within the Mn-Cu-O system, the reaction reversibility is poor for the compositions ranging from 0 \(\leq\) x(Cu) \(\leq\) 0.3 (Figure 5a), as the material transitions through the hausmannite phase during the reduction step, with low re-oxidation capability [36], as also previously observed for Mn-based systems [35; 36]. The hausmannite phase can be avoided completely by choosing a composition within the range 0.4 \(\leq\) x(Cu) \(\leq\) 0.8, for which a good cycling stability is then observed, along with an increased OSC when compared to pure Mn\({}_{2}\)O\({}_{3}\). The oxygen exchange capacity of a Mn-Cu mixed oxide with 34 wt% CuO was also investigated in a fluidized bed reactor and in TGA, and results showed complete cyclability for 18 cycles over the course of 35 h (reduction in N\({}_{2}\)/oxidation in 10 vol% O\({}_{2}\):N\({}_{2}\)) [48]. The addition of copper to Mn\({}_{2}\)O\({}_{3}\) thus improves the cyclability of the material for x(Cu) > 0.3, but the sintering issue remains at high temperature and increases with increasing amounts of added copper [36].  
The energy storage capacity of the Mn-Cu-O mixed oxides was found equal (148 kJ/kg for x(Cu) = 0.4 and 0.5) or higher (354 kJ/kg for x(Cu) = 0.8) than that of pure Mn\({}_{2}\)O\({}_{3}\) for compositions with x(Cu) > 0.4 (Figure 5b). In order to avoid sintering and to keep the cost of the material at a minimum, mixed oxides of Mn-Cu-O with low copper amounts are preferred [36; 41]. [PERSON] et al. (2016) [41] suggest that the composition at x(Cu) = 0.5 is also an option because of its melting point shifting from 1070 \({}^{\circ}\)C for the redox reaction of pure copper oxide to 1180 \({}^{\circ}\)C.  
The reaction temperature is also modified by the addition of copper, as compositions with x(Cu) \(\leq\) 0.3 show lower reduction temperature than Mn\({}_{2}\)O\({}_{3}\) (Figure 5c). The gap in temperature between oxidation and reduction is especially reduced for x(Cu) = 0.4 and x(Cu) = 0.8.  
Figure 5: Mass variation (OSC), enthalpy change and onset temperatures of redox reactions measured for the Mn-Cu-O system.
## 5 Simulation
### Increasing Number of Connections
#### 5.1.2 Average Packet Loss  
As shown in Figure 11, WEAMR with hotline-assisted approach shows much more reliable results compared to purely _ad hoc_-based routing. We can notice that as the numbers of connections are increasing there is a decrease in the packet success ratio in both the cases, however, the packet drop rate is much higher in AODV and AOMDV as compared to WEAMR. One of the main reasons is that WSN nodes communicate in a wireless medium and with increasing number of hops the chances of packets loss increase exponentially. With a smaller number of connections, the difference between AODV and AOMDV is not very noticeable. However, as the number of connections increase, AOMDV performs better than legacy AODV due to the multipath use, but overall WEAMR outperforms AOMDV due to hotline-assisted routing which has better delivery throughput due to the use of a wired medium. Hotlines bypass erroneous wireless links and make every gateway just one hop away from all other gateways [26]. Therefore, the proposed topology enhances the packet success ratio manifolds as compared to traditional _ad hoc_ wireless routing as shown in Figure 11.  
Figure 11: Average packet loss in end to end with increasing number of connections.  
Figure 10: Average end to end delay with increasing number of connections.
# Adaptive Learning Approach for Human Activity Recognition Using Data from Smartphone Sensors
###### Abstract  
Every day humans interact with smartphones that have embedded sensors that enable the tracking of changing physical activities of the device owner. However, several problems arise with the recognition of multiple activities (such as walking, sitting, running, and other) on smartphones. Firstly, most of the devices do not recognize some activities well, such as walking upstairs or downstairs. Secondly, recognition algorithms are embedded into smartphone software and are static, unless updated. In this case, a recognition algorithm must be re-trained with training data of a specific size. Thus, an adaptive (also known as, online or incremental) learning algorithm would be useful in this situation. In this work, an adaptive learning and classification algorithm based on hidden Markov models (HMMs) is applied to human activity recognition, and an architecture model for smartphones is proposed. To create a self-learning method, a technique that involves building an incremental algorithm in a maximal likelihood framework has been developed. The adaptive algorithms created enable fast self-learning of the model parameters without requiring the device to store data obtained from sensors. It also does not require sending gathered data to a server over the network for additional processing, making them autonomous and independent from outside systems. Experiments involving the modeling of various activities as separate HMMs with different numbers of states, as well as modeling several activities connected to one HMM, were performed. A public dataset called the Activity Recognition Dataset was considered for this study. To generalize the results, different performance metrics were used in the validation of the proposed algorithm.  
Keywords:human activity recognition; hidden Markov models; HMM; smartphone sensors; unsupervised learning +
Footnote †: journal: Applied sciences