esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Crowd Participation Features on Mobile Edge Computing
## 1 Introduction  
With the rapid progress in communication and sensor technologies and the continuous pervasion of portable devices, we are entering the era of the Internet of Things (IoT) [1]. Many interesting applications such as location-based recommendation and augmented reality are emerging [2]. All of these require a better quality of experience because there will be limited time to wait for responses from remote cloud servers. Mobile edge computing (MEC) [3], which is one of the key technologies of IoT, bridges the gap between the real and virtual spaces and caches content in local or even in the edges of networks, so as to efficiently reduce the backhaul delay and improve the network capacity [4]. As shown in Figure 1, the user terminals (human/vehicles) can connect directly to the network via access points (AP) and receive the desired content from their neighbors or the local edge servers [5].  
Different from the content distribution networks, MEC places content close to end users, while the content distribution networks mainly focus on publishing and subscribing of content. On the other hand, unlike traditional cloud computing technology, where the capabilities of the cloud servers play a big role, crowd involvement is one of the key factors leading to success in MEC. The dynamics of crowd behavior (mobility, preferences, etc.) is a double-edged sword [6; 7]. From a negative perspective, it introduces new challenges in network management. For example, it changes the network topology over time, and the journey of content experiences intermittent connected links [8; 9]. From a positive perspective, it brings many unprecedented opportunities ranging from better scalability of networks to low cost of deployment [10]. There are some works that consider the mobility or the sociality of nodes to form clusters, so as to exchange content among nodes efficiently [11; 12]. For example, the authors of [13; 14] employed features such as interest, energy and the physical distance proximity to classify nodes and improve the quality of service.  
In this article, we specifically investigate how the crowd features can be exploited in the following two aspects:  
* Opportunistic caching: The application can independently decide which content should be stored and shared, without active participation from end users.
* Opportunistic transmission: The routing path cannot be decided in advance, and the content is delivered in a store-carry-forward style.  
In the following sections, we first discuss the opportunistic caching from the perspective of cache size, number of caching copies and content-allocation schemes. We then investigate the opportunistic transmission from the perspectives of the control and data planes, followed by an illustrated experiment. Finally, the conclusion and some open issues are presented in Section 5.
# A Predictive Method for Temperature Based on Ensemble EMD with Linear Regression
## 5 Experimental Procedures
### Assessment of Experiment Result of Other Methods
#### 5.2.5 Kr
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Method** & **Sin (\(\times\)10\({}^{4}\))** & **Temperature (\(\times\)10\({}^{4}\))** \\ \hline \multirow{8}{*}{RFR} & AIC & \(-\)4.404751993 & \(-\)2.25989432 \\  & SBIC & \(-\)4.361883637 & \(-\)2.217025965 \\  & HQIC & \(-\)4.389864135 & \(-\)2.245006462 \\  & AICc & \(-\)4.404611618 & \(-\)2.259753945 \\  & AIC & \(-\)4.404656731 & \(-\)0.850477217 \\  & SBIC & \(-\)4.361788376 & \(-\)0.807608862 \\ KNR & HQIC & \(-\)4.389768873 & \(-\)0.83558936 \\  & AICc & \(-\)4.404516356 & \(-\)0.850336842 \\  & AIC & \(-\)4.403659541 & \(-\)5.881639971 \\ \multirow{2}{*}{NuSVR} & SBIC & \(-\)4.360791186 & \(-\)5.924508327 \\  & HQIC & \(-\)4.388771684 & \(-\)5.896527829 \\  & AICc & \(-\)4.403519167 & \(-\)5.881780346 \\  & AIC & \(-\)4.404620117 & \(-\)2.390680263 \\ \multirow{2}{*}{GBR} & SBIC & \(-\)4.361751761 & \(-\)2.347811907 \\  & HQIC & \(-\)4.389732259 & \(-\)2.375792405 \\  & AICc & \(-\)4.404479742 & \(-\)2.390539888 \\ \multirow{2}{*}{KR} & ASIC & \(-\)3.701476887 & \(-\)74.11238098 \\  & SBIC & \(-\)3.658608532 & \(-\)74.15524934 \\  & HQIC & \(-\)3.68658903 & \(-\)74.12726884 \\ \multirow{2}{*}{LSTM} & ASIC & \(-\)3.701336513 & \(-\)74.11252136 \\  & AIC & \(-\)3.981176098 & \(-\)67.08503466 \\  & SBIC & \(-\)3.98307743 & \(-\)67.12790301 \\ \multirow{2}{*}{LSTM} & HQIC & \(-\)3.96628824 & \(-\)67.09992252 \\  & AICc & \(-\)3.981035723 & \(-\)67.08517503 \\ \multirow{2}{*}{EEMD-LSTM} & ASIC & \(-\)4.252731215 & \(-\)4.781901631 \\  & SBIC & \(-\)4.209862859 & \(-\)4.824769987 \\ \multirow{2}{*}{EEMD-LSTM} & HQIC & \(-\)4.237843357 & \(-\)4.796789489 \\  & AICc & \(-\)4.25259084 & \(-\)4.782042006 \\ \multirow{2}{*}{LR} & ASIC & \(-\)4.404780518 & \(-\)2.465323208 \\  & SBIC & \(-\)4.361912162 & \(-\)2.422454853 \\ \multirow{2}{*}{LR} & HQIC & \(-\)4.38989266 & \(-\)2.450435351 \\  & AICc & \(-\)4.404640143 & \(-\)2.465182834 \\ \multirow{2}{*}{Proposed} & ASIC & \(-\)4.404780518 & \(-\)3.896197596 \\ \multirow{2}{*}{EEMD-LR} & SBIC & \(-\)4.361912163 & \(-\)3.853329241 \\  & HQIC & \(-\)4.389892661 & \(-\)3.881309739 \\ \multirow{2}{*}{LRT} & ASICc & \(-\)4.404640144 & \(-\)3.896057222 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Criteria values of nine approaches for two data sets.
## 4 Numerical Experiments, Results and Discussions
### Discussions of the Results Obtained by the Proposed Algorithm  
It can be seen from Table 2, for the former nine instances, the proposed algorithm (DICA1) found the known optimal solution, and for last three instances, though the known optimal solutions are not found, the best result found by the proposed algorithm are very close to the known optimal solutions. Their deviations with the corresponding known optimal solution are only 0.0421%, 1.0311% and 2.4544%, respectively. The average of the results for every instance is also quite close to the known optimal solution. Only for eil101, pr1323 and fl1400, the deviation with the known optimal solution exceeds 1%. For all the former ten instances except eil101, the probability of finding a solution which is within 1% deviation with the known optimal solution can reach more than 80%, especially, for berlin52, pr76, kroA100 and kroB150, it reaches 100%. In addition, for berlin52, the proposed algorithm found the known optimal solution in every test.
## 2 Methods
### Eligibility Criteria  
Studies were eligible for inclusion if they met the following population, concept, and context (PCC) criteria.  
Population (P): The studies included adults diagnosed with Parkinson's disease (PD). The eligible participants were those with confirmed PD who could ambulate independently (without assistive devices) and were classified at Hoehn and Yahr stages \(\leq 4\), indicating mild to moderate motor impairment. The studies focusing on populations with significant comorbidities that could affect motor function, such as musculoskeletal, rheumatic, or severe cardiorespiratory conditions, were excluded to ensure that the results specifically reflected the outcomes related to PD.  
Concept (C): The primary concept of interest was the use of cueing strategies as an intervention to improve motor function. Cueing strategies involve the use of external stimuli--visual, auditory, or somatosensory cues--aimed at facilitating motor performance. Studies were included if they applied cueing as a standalone intervention or combined it with other non-pharmacological treatments, provided that cueing was the distinct component in at least one group. Studies that used cueing during the \"ON\" phase of dopaminergic therapy (when symptoms are generally better controlled) were prioritized, as this setting maximizes the potential observable effect of the cues on motor outcomes.  
Context (C): Eligible studies focused on evaluating cueing strategies within clinical or rehabilitation settings aimed at improving specific outcomes: gait, balance, and quality of life in individuals with PD. Studies conducted in various settings--clinical, outpatient, or home-based--were included to capture a range of environments where cueing interventionsmay be applied. Additionally, only randomized controlled trials (RCTs) were considered, given their high level of evidence. The studies conducted solely in exploratory or quasi-experimental designs were excluded to maintain methodological rigor.
# Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk Management
## 3 Applications of Artificial Intelligence in Viticulture
### Data-Driven Approaches for Optimizing Water and Nutrient Management
#### 3.4.1 Water Management  
The management of water resources is a fundamental aspect of viticulture, exerting a significant influence on the yield and quality of grapes. AI technologies enable precise irrigation management by analyzing data from soil moisture sensors, weather forecasts, and plant stress indicators. ML models can predict the optimal irrigation schedules, ensuring vines receive the right amount of water at the right time, thus preventing both under- and over-irrigation [66]. By integrating data from remote sensing, ground-based sensors, and weather stations, AI systems create a comprehensive picture of the vineyard's water needs, allowing for more efficient water application and conservation of this valuable resource [67].  
For example, a study in New Zealand demonstrated the effectiveness of a multi-agent system for intelligent irrigation management. This system maximized water sharing within a community by accurately estimating water needs based on crop type, farm size, and other factors, leading to more efficient water use [9]. Additionally, a vineyard in Italy implemented an AI-driven irrigation management system that reduced water usage by 20% while maintaining or improving grape quality [66]. In Portugal, the SIMDualKc model was used to evaluate irrigation water management under scarcity in Mediterranean vineyards. This study demonstrated improved water use efficiency and better soil water balance, contributing to sustainable viticulture practices [68]. These examples highlight the ability of AI-driven water management systems to conserve water resources while maintaining high standards of grape quality [69].
# A Distributed Approach to the Evasion Problem
## 2 Materials and Methods
### Distributing the Algorithm  
As we argue in Section 2.3, it is enough for each node in a distributed setting to keep track of its Delaunay neighbors and their Delaunay neighbors. This observation motivates the following extension of [PERSON] algorithm.  
Each mobile sensor \(v\) needs to maintain a local extract \(\mathcal{D}\) of the Delaunay graph of the nodes, with \(DN(v)\) representing Delaunay neighbors, and \(\alpha\)-complex \(\mathcal{A}\), with \(N(v)\) denoting the \(\alpha-complex\) neighbors. At each time tick \(t_{i}\), each sensor \(v\) will update its location, perform the actions defined in Algorithm 2, and exchange messages with its neighbors \(DN(v)\). We list all the possible events in Table 1.  
Figure 1: Evaluation of _False_ and _True_ zones with [PERSON] algorithm. _True_ zones marked green, _False_ zones marked red. Delaunay neighbors shown with green edges, actual neighbors shown with red edges.  
```
1: Send location update to \(DN(v)\)(broadcasting to \(N(v)\), and via routing to \(DN(v)-N(v)\)).
2: Send neighbor's current location to \(DN(v)\).
3: Update Delaunay graph with most recent information from neighbors.
4:if Del-\(\alpha\)then
5: Notify all nodes on the boundary of the region that changes.
6:if\(\alpha\)-Del then
7: Notify all nodes on the boundary of the region that changes.
8:if\(\Delta+\)then
9: Mark the triangle as _True_ or _False_ accordingly.
10:if Nbr+ then
11: Sensor \(v\) becomes neighbor with \(u\), \(u\) and \(v\) exchange the list of neighbors, and notify their neighbors the location of new neighbor.
12:if Nbr- then
13: Sensor \(v\) loses its neighbor \(u\)(and vice versa), \(u\) and \(v\) notify their neighbors the updated list of neighbors.
```  
**Algorithm 2** Distributed Evasion Detection.  
To compute \(\mathcal{D}\) and \(\mathcal{A}\), two-hop neighbor information will be used, and thus each sensor only need to communicate with its Delaunay neighbors, we will show that 2-hop neighbor information is suffice for our purpose in Section 2.3.
## 5 Results and Analysis
### Tracking Performance  
The tracking results of the four candidate trackers on different subsets of the NPSPT benchmark are presented in Table 5. Compared with the performance under the MOT17  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithms** & **HOTA \(\uparrow\)** & **MOTA \(\uparrow\)** & **IDF1 \(\downarrow\)** & **IDSW \(\downarrow\)** \\ \hline Tracker++ & 44.0 & 50.6 & 51.8 & **1708** \\ CenterTrack & 51.3 & 66.9 & 63.0 & 3107 \\ FairMOT & 58.5 & 72.0 & **70.3** & 3391 \\ TransTrack & 52.8 & **74.1** & 61.6 & 3713 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the candidate methods under the “private detector” protocol on MOT17 training set.  
dataset, performance of all algorithms under the NPSPT have different degrees of score degradation. Among which, Trackor++ and TransTranck have the worst scalability, with more than 20 points drop in HOTA.  
The main source of the performance degradation of Trackor++ comes from the incompatibility of the faster-rcnn structure for large-size images, which the algorithm relies on in both the detection and data connection stages.  
A huge number of model parameters makes the association part of TransTrack very complicated and hard to train, leading to the input size being larger and the tracking performance worse.  
FairMOT and CenterTrack are not sensitive to the input size. CenterTrack mainly relies on the positions of targets in the current frame to predict the positions and relationships of targets in the next frame. The relatively small ratio of the target box in NPSPT makes the prediction more difficult. In contrast, FairMOT's built-in ReID feature branch handles the problem of adjacent frame association better. Therefore, even though the detection performance of these two are close, the HOTA score of FairMOT is significantly higher than that of CenterTrack.  
Contrary to initial intuition, 5-a-side matches are not the easiest scenarios, and 11-a-side scenarios are not the most difficult scenarios. The difficulty of tracking and detection is not entirely directly related to the number of players and the size of the input image. It is related to the arrangement of the cameras, the ratio of the target box in the image, and other factors. These experimental results demonstrate that, even the top algorithm FairMOT is far from perfect, and more efforts are needed to improve the current tracking methods to handle challenging situations in this scenario.
# Towards the Feasibility of Long Range Wireless Power Transfer over an Ocean Surface
## 3 Results
### Wireless Power Transfer under Different Salinity Levels  
In this test, the salinity level of the water in the experimental setup was changed from zero to the seawater's salinity level at 33 g/L. The transmission efficiency was measured under different salinity levels. Figure 4 shows the measured transmission efficiency as a function of salinity.  
As Figure 4 shows, we only need 4 g/L of salt concentration to elevate the measured transmission efficiency to almost 100%.
# Insights into the Issue of Deploying a Private LoRaWAN
## 1 Introduction
### Contribution and Structure of This Study  
Many works focus on the technology itself or deal with either private or public network independently, as we discuss further in Section 2. The main contribution of this work is that we address both these deployment options and highlight the difference between them, with respect to three major metrics: the network performance, security, and costs. Namely, we first conduct an experimental measurement campaign to estimate the coverage and signal levels of the private and public LoRaWAN networks in the campus of Brno University of Technology, and compare the results of the two networks. Second, we deliver the analysis of the security procedures specified in the different LoRaWAN specification releases, and discuss the security aspects relevant to private and public network deployments. Third, we deliver the model and identify the cost components allowing to estimate the costs of LoRaWAN deployment and owning, and detail the steps one has to take to make a decision whether to go for public or private LoRaWAN deployment. To the best of our knowledge, the current work is the first one which analyzes and compares the two LoRaWAN deployment options, thus supporting selection of the one most suitable.  
The rest of the paper is organized as follows: Section 2 provides an in-depth analysis of the advances in LPWAN focusing on the LoRaWAN technology. Section 3 introduces the experimental environment used for our measurements and experiments, while the main contribution (technological, security, and deployment evaluation) is included in Section 4. Finally, Section 5 summarizes our conclusions.
###### Abstract  
In this work, we synthesized new materials based on Fe(II) phthalocyanine (FePc), urea and carbon black pearls (BP), called Fe-N-C, as electrocatalysts for the oxygen reduction reaction (ORR) in neutral solution. The electrocatalysts were prepared by combining ball-milling and pyrolysis treatments, which affected the electrochemical surface area (ECSA) and electrocatalytic activity toward ORR, and stability was evaluated by cyclic voltammetry and chronoamperometry. Ball-milling allowed us to increase the ECSA, and the ORR activity as compared to the Fe-N-C sample obtained without any ball-milling. The effect of a subsequent pyrolysis treatment after ball-milling further improved the electrocatalytic stability of the materials. The set of results indicated that combining ball-milling time and pyrolysis treatments allowed us to obtain Fe-N-C catalysts with high catalytic activity toward ORR and stability which makes them suitable for microbial fuel cell applications.  
iron phthalocyanine; ball-milling; M-N-C electrocatalysts; oxygen reduction reaction; fuel cells +
Footnote †: journal: Applied Sciences
## 4 Security
### Confidentiality of Information  
The wireless nature of the communication between the sensors and devices eases these attacks, as there is no strict physical boundary of the transmission medium. An attacker located close to the network can thus easily eavesdrop the communication and threaten the confidentiality of the transmitted information. The content of the packets can be revealed to the attacker, who can benefit from stolen information like network configuration data to conduct further attacks. Eavesdropping can also be coupled with network monitoring to perform traffic analysis. The aim of this attack is to determine the responsibility of each sensor and identify the data sink for example. An analysis of the packet content is not mandatory to success; the amount of exchanged packets can be a sufficient clue [39]. However, an attack directed against the data sink can be very efficient, as the entire data set may be damaged or lost.
# Throughput/Area Optimized Architecture for Elliptic-Curve Diffie-Hellman Protocol
## 3 Proposed ECDH Architecture  
The proposed ECDH architecture is shown in Figure 3. It consists of two routing networks (\(RoutingNetwork_{1}\) and \(RoutingNetwork_{2}\)), an arithmetic and logic unit (ALU), a memory unit, two pipeline registers (\(PR_{1}\) and \(PR_{2}\)) and two finite state machine based controllers (_Controller-1_ and _Controller-2_). The details of these units are given as:
## 7 Effects of the Interventions into Microbiota on the Micronutrient Status of Humans  
The dysbiosis of intestinal microbiota and micronutrient deficiencies co-occur with obesity. Interventions in the microbiota to improve the nutritional status of populations suffering from micronutrient malnutrition may thus be an idea worth considering. The two most researched types of such interventions are the supplementation of the diet with probiotics and/or prebiotics. Both are targeted to change the human intestinal microbiota, which consequently may affect the bioavailability of nutrients, and some such interventions were shown to affect the micronutrient levels in humans positively. Unfortunately, most of the published evidence involved healthy people or groups with illnesses not directly associated with obesity. A systematic review of studies that looked at the effect of probiotic supplementation on the micronutrient status of healthy humans found 14 works published up until June 2020 [160]. Reviewed studies reported a significant increase in blood levels of vitamins B9 and B12, as well as Fe, Zn, and Ca, following the administration of probiotics (in the case of a single study of a symbiotic [161]) and ambiguous effects for vitamins A, D, and E and carotenoids. These works were too heterogeneous in terms of measured outcomes, researched populations, and intervention conditions, including applied probiotics, to perform a meta-analysis [160]. The type of probiotic is a very important factor affecting the efficacy of the intervention. For example, a meta-analysis of studies published until February 2019, showed that the use of the probiotic _L. plantarum_ 299v increased the absorption of non-heme Fe from the diet in healthy white Europeans, mainly women [162]. However, the potential effect of probiotics other than _L. plantarum_ 299v on Fe absorption was questionable since none of them had a significant effect on Fe status. Several studies described the effect of prebiotics and potentially prebiotic substances on the micronutrient status of healthy humans in a variety of populations. For example, [PERSON] et al. demonstrated the positive effects of galactooligosaccharides (GOS) and soluble corn/maize fiber on the absorption of Ca in adolescents [163; 164; 165]. In postmenopausal women, different authors found that fructoligosaccharide (FOS) supplementation resulted in increased absorption of Mg and Cu but not Se and Zn [166; 154]. In addition, administering inulin to infants fed with formula resulted in increased apparent absorption of Zn, Mg, and Fe but not Ca [167; 168]. On the other hand, the improvement of Ca blood levels in response to inulin was observed in young, healthy males [168]. Only a few studies looked at the effects of pro- and/or prebiotic supplementation on the micronutrient status in people with excess weight and/or associated health conditions. A summary of these studies is presented in Table 7.
## 4 Model Validation and Results Analysis
### Prediction Results Analysis  
The data used in our experiments are the tanker data in the dataset with a time interval of 60 s, which contains 61 tankers with a total of 725,069 records, of which 80% are classified as the training set and 20% as the test set.