Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of the Mobility of Heavy Metals in Sludge for the Sewage Treatment Plant in Daleszyce

Abstract Sewage sludge is a by-product of wastewater treatment processes. However, it has high fertilising and soil-forming properties, but it cannot always be used for this purpose. The two main criteria limiting their natural use are heavy metals and parasite eggs. Sewage sludge taken from the Daleszyce wastewater treatment plant has been analysed for heavy metals. For this purpose a space analysis was performed to divide the total metal content into four mobility fractions. The mobility issue determines the ability of an element, or one of its forms, to move in the environment. The studies were performed using a four-stage BCR procedure. The results were analyzed and compared to the limits applicable in Poland for sewage sludge intended for environmental use.
Analysis of EMG Signals during Stance and Swing Phases for Controlling Magnetorheological Brake applications

Abstract The development of ankle foot orthoses (AFO) for lower limb rehabilitation have received significant attention over the past decades. Recently, passive AFO equipped with magnetorheological brake had been developed based on ankle angle and electromyography (EMG) signals. Nonetheless, the EMG signals were categorized in stance and swing phases through visual observation as the signals are stochastic. Therefore, this study aims to classify the pattern of EMG signals during stance and swing phases. Seven-time domains features will be extracted and fed into artificial neural network (ANN) as a classifier. Two different training algorithms of ANN namely Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG) will be applied. As number of inputs will affect the classification performance of ANN, different number of input features will be employed. In this study, three participants were recruited and walk on the treadmills for 60 seconds by constant the speed. The ANN model was designed with 2, 10, 12, and 14 inputs features with LM and SCG training algorithms. Then, the ANN was trained ten times and the performances of each inputs features were measured using classification rate of training, testing, validation and overall. This study found that all the inputs with LM training algorithm gained more than 2% average classification rate than SCG training algorithm. On the other hand, classification accuracy of 10, 12 and 14 inputs were 5% higher than 2 inputs. It can be concluded that LM training algorithm of ANN was performed better than SCG algorithm with at least 10 inputs.
Monotonicity and non-monotonicity regions of topological entropy for Lorenz-like families with infinite derivatives

Abstract We study behavior of the topological entropy as the function of parameters for two-parameter family of symmetric Lorenz maps Tc,ɛ(x) = (−1 + c|x|1−ɛ) · sgn(x). This is the normal form for splitting the homoclinic loop in systems which have a saddle equilibrium with one-dimensional unstable manifold and zero saddle value. Due to L.P. Shilnikov results, such a bifurcation corresponds to the birth of Lorenz attractor (when the saddle value becomes positive). We indicate those regions in the bifurcation plane where the topological entropy depends monotonically on the parameter c, as well as those for which the monotonicity does not take place. Also, we indicate the corresponding bifurcations for the Lorenz attractors.
Vertical Distribution of Soil Free-living Nematode in a Playa Habitat in the North-Western Negev Desert, Israel

Summary In order to investigate the vertical distribution of soil nematode community under a playa area in the Nizzana inter-sand dune area in the north-western Negev Desert of Israel, soil samples were collected from 0 – 10, 10 – 20, 20 – 30, 30 – 40, and 40 – 50 cm depths in the playa and loessial plain area (as control) during the dry and wet seasons. Each of soil samples was determined for soil moisture, organic matter concentration, electrical conductivity, and nematode community structure and biodiversity. The results revealed that soil moisture, organic matter, electrical conductivity, total nematode abundance and trophic groups were significantly influenced by sampling positions (P < 0.0001) and depths (P < 0.05) and significant interaction effect of position and depth (P < 0.05) was also found. Total number of nematodes in the playa area ranged from 1 to 15 individuals per 100 g dry soil, whereas they ranged from 60 to 631 individuals per 100 g dry soil in the loessial plain area. The greatest nematode number was found in 0 – 10 cm soil depth and the lowest nematode number was found in 40 – 50 cm soil depth. Total number of nematodes, bacterivores, fungivores, plant parasites and omnivores-predators were significantly (P < 0.05) greater in the loessial plain area than that in the playa area in all sampling depths under the dry and wet seasons. Total thirteen genera were found in the playa area, with Acrobeloides as the dominant genus, whereas there were fifty-five genera, with Acrobeles as the dominant genus in the loessial plain area. Nematode ecological indices, such as fungivores/bacterivores ratio (F/B), nematode channel ratio (NCR), trophic diversity (T), Shannon Index (H’), genus dominance (λ), species richness (SR), maturity index (MI), modified maturity index (MMI), Enrichment Index (EI) and Structure Index (SI) were significantly (P < 0.01) differentiated between sampling locations. As conclusion, soil free-living nematode inhabited mostly the upper soil layers (0 – 20 cm) and the number of nematode had gradually decreasing trend with soil depths increasing in the playa area. Moreover, each soil layer had a little nematode abundance and community diversity in the playa area in contrast to loessial plain area during the dry and wet seasons. Sampling sites and depths significantly effected on soil properties, nematode abundance and trophic groups, but nematode ecological indices were affected only by sampling sites.
Capacity of a LoRaWAN Cell

In this paper, we consider the problem of evaluating the capacity of a LoRaWAN cell. Previous analytical studies investigated LoRaWAN performance in terms of the Packet Delivery Ratio (PDR) given a number of devices around a gateway and its range. We improve the model for PDR by taking into consideration that the following two events are dependent: successful capture during a collision and successful frame decoding despite ambient noise. We consider a realistic traffic model in which all devices generate packets with the same inter-transmission times corresponding to the duty cycle limitation at the highest SF, regardless of the distance to the gateway. Based on the developed model, we optimize the Spreading Factor (SF) boundaries to even out PDR throughout the cell. We validate the analytical results with simulations, compare our model with previous work, and experimentally validate the hypothesis of Rayleigh fading for the LoRa channel. The important conclusion from our results is that a LoRa cell can handle a relatively large number of devices. We also show that there is practically no inter-SF interference (cross interference between transmissions with different SFs): interference from higher SFs comes from nodes located farther away, so they face greater attenuation and thus, they do not interfere with lower SF nodes.
Hydrochemical differences between river water and groundwater in Suzhou, Northern Anhui Province, China

Abstract Understanding hydrological process of surface water and groundwater is significant for the management of urban water resources. In this study, a total of thirty-seven water samples have been collected from the river (RW, 15 samples), shallow aquifer (SG, 12 samples), and deep aquifer (DG, 10 samples) in Suzhou, Northern Anhui Province, China, and their major ion concentrations and stable H–O isotopes have been measured. The results revealed that Na+ and HCO3− were the dominant cation and anion, respectively, and most of the water samples are classified to be Na-HCO3 type, to a lesser extent, Mg-HCO3 type. K-mean and Q-type clustering analyses ruled out the hydrological relationship between river and groundwater, but there was a significant connectivity between shallow and deep groundwater, which was further confirmed by the hydrogen and oxygen isotopes. The relationship between δ2H and δ18O has shown that precipitation was the main source of the groundwater in the study area. Furthermore, the values of deuterium excess (d-excess) in different water bodies suggested that the groundwater has not been affected by evaporation, which was the main process controlling the isotopic composition of river water.
The Response to the Training Load in the Aquatic Environment in Terms of the Stimulation of the Reflective Capabilities of Elite Synchronized Swimmers

Summary The youngest swimming sport included in the Summer Olympic Games since 1984 is synchronized swimming. Since the synchronized swimming is still growing popularity and professionalization, it is important to search for ways to improve sports performance. There are few scientific studies focusing also on the biological and motor indicators of top athletes. The present study examined biological and motor variables of elite synchronized swimmers (SYN, N = 13) in ages of 16.5 ± 3.23 years and compare the frequency of Angiotensin Converting Enzyme (ACE) gene genotypes among elite female synchronized swimmers and the non-athletic control group (CON, N = 30) in ages of 16.0 ± 0.6 years. The motor variables were measured using Optojump system before and after water training session. All measurements were collected by trained data collection staff. The ACE I/D variation differences between groups were identified by Chi-Square test. The results of motor variables obtained were evaluated statistically using the Wilcoxon Signed-Rank Test. The strength of association between selected biological and motor variables was measured by Spearman’s correlation. We provided evidence for significant differences of variation of the ACE I/D polymorphism between observed groups. A significant correlation among biological and motor parameters of SYN was demonstrated among the percentage of fat and the time of reflection (p = 0.042), the basal resting heart rate and the jump height (p = 0.006) and among the basal resting heart rate and the power (p = 0.012). The SYN significantly increased only their contact time in jumping (p < 0.016) after the training session. Based on the results we state that the effect of intervention in the stimulation of the reflective capabilities due to the training session in the aquatic environment was not confirmed in the study.
Verification Schemes of Multi-SIM Devices in Mobile Communication Systems

Multi-SIM capable mobile phones have been gaining popularity. Due to the absence of standardized solution, there are implementation variations in the User Equipment (UE) from both hardware and software perspectives. Especially, coordination of call events on multiple SIMs requires standardization as it involves interaction between the Core Network (CN) and the UE to avoid negative user experience such as missed incoming call or interrupted data session. 3rd Generation Partnership Project (3GPP) has an ongoing work to addresses this area in Release 17. However, security aspects are outside the scope, including mechanisms to verify multiple SIMs in a device. In this paper, we address this gap by proposing variations of verification schemes using crypgographic operations to help mobile networks to identify the device capability of the UE and verify multiple SIMs in it. The proposed mechanisms are simple and effective to meet the goal. Based on our investigation, we believe this is the first proposal to address this aspect in the multi-SIM devices in the mobile system.
Group Fairness in Committee Selection

In this article, we study fairness in committee selection problems. We consider a general notion of fairness via stability: A committee is stable if no coalition of voters can deviate and choose a committee of proportional size, so that all these voters strictly prefer the new committee to the existing one. Our main contribution is to extend this definition to stability of a distribution (or lottery) over committees. We consider two canonical voter preference models: the Approval Set setting where each voter approves a set of candidates and prefers committees with larger intersection with this set; and the Ranking Representative setting where each voter ranks committees based on how much she likes her favorite candidate in a committee. Our main result is to show that stable lotteries always exist for these canonical preference models. Interestingly, given preferences of voters over committees, the procedure for computing an approximately stable lottery is the same for both models and therefore extends to the setting where some voters have the former preference structure and others have the latter. Our existence proof uses the probabilistic method and a new large deviation inequality that may be of independent interest.
Maximizing the Quality of User Experience of Using Services in Edge Computing for Delay-Sensitive IoT Applications

The Internet of Things (IoT) technology offers unprecedented opportunities to interconnect human beings. However, the latency brought by unstable wireless networks and computation failures caused by limited resources on IoT devices prevents users from experiencing high efficiency and seamless user experience. To address these shortcomings, the integrated MEC with remote clouds is a promising platform, where edge-clouds (cloudlet) are co-located with wireless access points in the proximity of IoT devices, thus intensive-computation and sensing data from IoT devices can be offloaded to the MEC network for processing, and the service response latency can be significantly reduced. In this paper, we study delay-sensitive service provisioning in an MEC network for IoT applications. We first formulate two novel optimization problems, i.e., the total utility maximization problems under both static and dynamic offloading task request settings, with the aim to maximize the accumulative user satisfaction of using the services provided by the MEC. We then show that the defined problems are NP-hard. We instead devise efficient approximation and online algorithms with provable performance guarantees for the problems. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising.
Research on the Perception of Calligraphy Time Sequence Based on Markov Chain

Sequential restoration relates to the psychological phenomenon that one can imaginatively reconstruct the writing process by observing the stroke traces on a piece of calligraphy. While traditional theory emphasizes this phenomenon as a unique aesthetic feature of calligraphy, and neuroaesthetics studies its biological basis, this paper introduces the idea of quantifying it. In order to quantify sequential restoration, and to explore the factors that affect it, a sequentiality quantization method based on Markov chain is proposed. First, beholders' perception of the sequential order of predefined marker points on the calligraphy work is modeled as a Markov chain. Then, the entropy rate of the Markov model is calculated to measure its uncertainty. Finally, the metric sequentialiy is defined as the normalized negative entropy rate. The feasibility of this method is verified through the actual measurement of the character "Zou". The effect of graphic transforms on the sequentiality of single brush stroke was studied, and the result shows that graphic transforms, including mirror and rotation, significantly affect sequentiality. The experiment also shows that the textural details of a brush stroke are not the primary factor in forming the sequential restoration experience, but the viewer's own experience of stroke order is more important.
Evaluation of Dynamic Route Planning Impact on Vehicular Communications with SUMO

Simulations are the first approach used by the research community to evaluate mobile ad hoc networks. Particularly, vehicular ad hoc networks (VANETs) are a singular type of mobile ad hoc networks that raise technical challenges, for instance, in the context of vehicular mobility models. When assessing VANETs, realistic vehicular models are essential to produce meaningful evaluation results. In this context, realistic vehicles' mobility includes re-routing capabilities that allow vehicles to re-compute their routes in front of specific traffic conditions (e.g., traffic jams). In this paper, we provide a thorough analysis of the influence of enabling re-routing properties on (i) the mobility of the vehicle and (ii) on the connections of the vehicular network. For this, we use the road traffic simulator SUMO to generate vehicular traces, and then we will analyze the connectivity of the vehicular network employing well-known graph metrics. Our results provide insights about the behavior of the vehicle's mobility and the nodes' connectivity.
Optimal Budget-Feasible Mechanisms for Additive Valuations

In this article, we show a tight approximation guarantee for budget-feasible mechanisms with an additive buyer. We propose a new simple randomized mechanism with approximation ratio of 2, improving the previous best known result of 3. Our bound is tight with respect to either the optimal offline benchmark or its fractional relaxation. We also present a simple deterministic mechanism with the tight approximation guarantee of 3 against the fractional optimum, improving the best known result of (2+ √ 2) for the weaker integral benchmark.
Towards a Ring Analogue of the Leftover Hash Lemma

Abstract The leftover hash lemma (LHL) is used in the analysis of various lattice-based cryptosystems, such as the Regev and Dual-Regev encryption schemes as well as their leakage-resilient counterparts. The LHL does not hold in the ring setting, when the ring is far from a field, which is typical for efficient cryptosystems. Lyubashevsky et al. (Eurocrypt ’13) proved a “regularity lemma,” which can be used instead of the LHL, but applies only for Gaussian inputs. This is in contrast to the LHL, which applies when the input is drawn from any high min-entropy distribution. Our work presents an approach for generalizing the “regularity lemma” of Lyubashevsky et al. to certain conditional distributions. We assume the input was sampled from a discrete Gaussian distribution and consider the induced distribution, given side-channel leakage on the input. We present three instantiations of our approach, proving that the regularity lemma holds for three natural conditional distributions.
Randomized Load Balancing under Loosely Correlated State Information in Fog Computing

Fog computing infrastructures must support increasingly complex applications where a large number of sensors send data to intermediate fog nodes for processing. As the load in such applications (as in the case of a smart cities scenario) is subject to significant fluctuations both over time and space, load balancing is a fundamental task. In this paper we study a fully distributed algorithm for load balancing based on random probing of the neighbors' status. A qualifying point of our study is considering the impact of delay during the probe phase and analyzing the impact of stale load information. We propose a theoretical model for the loss of correlation between actual load on a node and stale information arriving to the neighbors. Furthermore, we analyze through simulation the performance of the proposed algorithm considering a wide set of parameters and comparing it with an approach from the literature based on random walks. Our analysis points out under which conditions the proposed algorithm can outperform the alternatives.
Complexity vs. Performance in Granular Embedding Spaces for Graph Classification

The most distinctive trait in structural pattern recognition in graph domain is the ability to deal with the organization and relations between the constituent entities of the pattern. Even if this can be convenient and/or necessary in many contexts, most of the state-of the art classification techniques can not be deployed directly in the graph domain without first embedding graph patterns towards a metric space. Granular Computing is a powerful information processing paradigm that can be employed in order to drive the synthesis of automatic embedding spaces from structured domains. In this paper we investigate several classification techniques starting from Granular Computing-based embedding procedures and provide a thorough overview in terms of model complexity, embedding space complexity and performances on several open-access datasets for graph classification. We witness that certain classification techniques perform poorly both from the point of view of complexity and learning performances as the case of non-linear SVM, suggesting that high dimensionality of the synthesized embedding space can negatively affect the effectiveness of these approaches. On the other hand, linear support vector machines, neuro-fuzzy networks and nearest neighbour classifiers have comparable performances in terms of accuracy, with second being the most competitive in terms of structural complexity and the latter being the most competitive in terms of embedding space dimensionality.
Inferring Energy Consumption Patterns in Public Buildings

The advent of smart meters have radically changed the mechanisms traditionally used for energy consumption monitoring. The possibility of having (i) highly frequent readings (even every minute); (ii) accurate energy data gathering; and (iii) real time data exchange capabilities to share the energy consumption data with other elements in the Smart Grid, open the door to further deeper analysis within this context. In this paper, we focus on obtaining energy consumption patterns. Our approach combines clustering and predictive techniques in order to infer these patterns using data gathered from public buildings in a university campus. Our analysis allows us to infer that clustering is not an appropriate mechanism, since the use of buildings is mixed (administrative, labs, classrooms, etc.). However, predictive approaches give promising results, specially LSTM and XGBoost.
WiMove: Toward Infrastructure Mobility in mmWave WiFi

Line-of-sight (LOS) is a critical requirement for mmWave wireless communications. In this work, we explore the use of access point (AP) infrastructure mobility to optimize indoor mmWave WiFi network performance based on the discovery of LOS connectivity to stations (STAs). We consider a ceiling-mounted mobile (CMM) AP as the infrastructure mobility framework. Within this framework, we present a LOS prediction algorithm based on machine learning (ML) that addresses the LOS discovery problem. The algorithm relies on the available network state information (e.g., LOS connectivity between STAs and the AP) to predict the unknown LOS connectivity status between the reachable AP locations and target STAs. We show that the proposed algorithm can predict LOS connectivity between the AP and target STAs with an accuracy up to 91%. Based on the LOS prediction algorithm, we then propose a systematic solution WiMove, which can decide if and where the AP should move to for optimizing network performance. Using both ns-3 based simulation and experimental prototype implementation, we show that the throughput and fairness performance of WiMove is up to 119% and 15% better compared with single static AP and brute force search.