esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Amoeba Protection
### Effects on Bacterial Transcriptome  
In 1995, when analyzing the effect of _L__pneumophila_ grown inside amoebae and macrophages on the final bacterial MIC and its survival levels in the presence of antibiotics, [PERSON] et al. observed that in _L. pneumoniae_ released from amoeba, the MIC of ciprofloxacin did not increase with respect to in vitro cultured _L. pneumoniae_ (MIC = 0.06 \(\upmu\)g/mL). Nevertheless, when exposed to 1 \(\upmu\)g/mL of ciprofloxacin, survival rates increased 1000-fold [106]. Of note, after 48 h of in vitro growth, the survival ability of amoeba-grown _L. pneumoniae_ was lost [106]. No analysis of the mechanisms of ciprofloxacin survival acquired was performed, but the authors observed deep morphological changes including motility affection [106]. Although no analysis of quinolone targets was performed, all these findings strongly suggest a transient adaptative response and not a true quinolone-resistant mutant selection. Similarly, studies analyzing the intracellular activity of ancient quinolones against _Francisella noatunensis_ subsp. _noatunensis_ or _Burkholderia mallei_ have shown the inability of flumequine to inhibit _F. noatunensis_ replication, despite high flumequine intake within macrophages [98], and the inability of flumequine to completely clear _B. mallei_ irrespective of patient improvement [97].  
All these findings agree with the described effect of an intracellular lifestyle resulting in an altered bacterial transcriptome, which may lead to a different morphology, a modified duplication time or an altered surface [107; 108]. Of note, different studies have shown that the altered expression levels of genes apparently unrelated to the development of quinolone resistance may result in increased MIC levels to quinolones with a few genes increasing levels of quinolone efflux or impairing quinolone intake (see Section 8: Unconsidered Chromosomal Mutations).  
Furthermore, the expression of specific genes seems to be relevant for the ability of bacteria to survive and multiply within amoebae [109]. Among these genes, it has been observed that the lack of functionality of _cmeB_ impairs the survival and replication of amoeba-internalized _Campylobacter jejuni_[109]. CmeABC is an RND efflux pump present in the _Campylobacter_ spp. genome, which is able to efficiently extrude quinolones, among other antibacterial agents, with reports showing that _cmeABC_ overexpression promotes the selection of fluoroquinolone-resistant _C. jejuni_ isolates [110; 111]. Therefore, amoebae-fed resistance as well as active efflux of fluoroquinolones are co-selected characteristics of _Campylobacter_.
# Underlying Security Transmission Design for Orthogonal Time Frequency Space (OTFS) Modulation
## 4 Secret Key Generation Algorithm
### Characteristic Analysis for Secret Key  
The performance of the key can directly determine the security performance of the OTFS transmission. In this section, the main performance tests are for randomness and complexity. Let us first clarify the testing conditions as follows. The length of the sequence is L = 10,000. In each test, we select five sets of data streams for the GOST algorithm, the DES algorithm, and the chaotic mapping algorithm, ref. [26] respectively.
###### Abstract  
The aim of the study is to evaluate the effectiveness of two complementary mandibular repositioning splints (SVED and MORA) designed after a preliminary patients' posture-stabilometric evaluation in treatment for temporomandibular myofacial pain (TMP) using the Pain-Intensity Numeric Rating Scale (PI-NRS) assessment. Eighty-six consecutive sportive non-agonistic subjects with TMP were randomly divided in two groups. Forty-two wear mandibular repositioning splints designed by a posture-stabilometric evaluation, thus constituting the test group. The other 44 subjects were not subjected to any treatment, thus represented the control group. To record pain intensity, subjects in both groups were asked to fill in a PI-NRS which ranged from 0 (no pain) to 10 (worst imaginable pain). In the test group, PI-NRS was assessed three times: before starting therapy (t0), after 4 months (t1) and after 8 months (t2) of treatment. Instead, in the control group PI-NRS was recorded during the first visit (t0) and after 8 months (t2). The Friedman test showed a statistically significant decrease in PI-NRS mean scores after 4 (t1) and 8 (t2) months from the start of the pathological therapy with mandibular repositioning splints, (_p_ < 0.001) in test group. There is enough evidence to assess that occlusal splint therapy for mandibular repositioning (MORA and SVED) designed through a posture-stabilometric evaluation could be considered in the treatment of temporomandibularwofacial pain.  
temporomandibular disorders; occlusal therapy; posture; myofascal pain; personalized medicine
# Toward Improving the Reliability of Discrete Movement Recognition of sEMG Signals
## 3 Methods  
### Dataset  
The dataset applied in this study is from Ninapro database 1, Exercise A [35; 36; 38], which consists of 12 basic hand movements. The sEMG signals are collected using 10 active double-differential Otto-Bock Myo-Bock 13E200 electrodes. Eight electrodes are uniformly placed around the forearm. Additionally, two electrodes are placed on the flexor and extensor muscles of the forearm. The sampling rate of the sEMG signals is 100 Hz. The total number of subjects in the database is 27, which are all intact subjects. The movements used in this study are 12 finger movements, which are index flexion/extension, middle flexion/extension, ring flexion/extension, little finger flexion/extension, thumb adduction/abduction, thumb flexion/extension [36]. To evaluate the classifier's performance on trained movements, the first five repetitions of the 12 movements are used for classifier training, and the left five repetitions are used for testing.
## 4 Results
### Land Cover Map and Its Accuracy  
The Landsat OLI images captured on multiple dates from years 2013 to 2014 were classified into the following 12 land cover types: water bodies, urban, paddy fields, crop lands, orchards, grasslands, silver grass, bare lands, coniferous forests, evergreen broadleaf forests, deciduous broadleaf forests and snow/clouds. These detailed classes were then reclassified into the three general forest classes and one other-than-forest class, resulting in a total of four land cover categories. Only the forest categories are of interest; the accuracy assessment was also performed with this map. The final product of the land cover map (before reclassification) of Japan is shown in Figure 7.  
Figure 7: Land Cover map of Japan developed using 35 Landsat Operational Land Imager (OLI) scenes yielding 12 classes in total.  
The ground truth points obtained from Google Earth together with the reference vegetation map [39] were used in the resulting land cover map to perform an accuracy assessment by outputting an error matrix (Table 2). The final number of ground truth points yields 3550 points. However, the number of points was normalized for each class to 1000 points, to make calculations and interpretation easier in the accuracy assessment. The overall accuracy of the land cover (forest cover) map result was 73.93%, and the overall Kappa Index of Agreement (KIA) result was 0.65. KIA is a statistical measure adapted for accuracy assessment in the remote sensing fields by [PERSON] [40]. KIA values range from 0 to 1, with zero representing completely random chance and 1 meaning a perfectly true agreement. In this study, KIA = 0.65, which could be said to show that the map is 65% likely to be the result of true agreement.
## 2 System Model
The resulting first- and second-order statistics of the models are different due to these two different propagation environments. Therefore, we have selected the most widely-used channel models in their respective categories, i.e., Nakagami-\(m\) for the traditional wireless channel and shadowed Rice for the LMS channel. The evaluation of the distribution of the sum of squared shadowed-Rice random variables and its application to the LMS channel was carried out in [24]. The performance evaluation of an energy detector under multi-path fading/shadowing effects in a Gamma-shadowed Rician fading condition was done in [25]. Thus, we can see that the HSTS model under consideration alleviates the need of statistics for the sum of squared Nakagami-\(m\) random variables, required for multiple independent interferers [20]. As previously published results are either in the form of infinite sums or higher order derivatives  
Figure 1: A hybrid/integrated satellite-terrestrial system with co-channel interference (CCI). of the fading parameter \(m\), this makes the resulting modeling and analysis impractical and quite difficult to realize computationally [20]. Our work captures the essence of HSTS in [20] with the dominant single interferer, reduces the resulting approximations assumed therein and extends it to multiple dimensions.
## 2 Simulation Methods
### Molecular Simulation  
Molecular dynamics simulation is a computational methodology rooted in classical mechanics, specifically utilizing molecular force fields to numerically integrate the equations of motion for a molecular system. This approach enables the investigation of the structural organization and properties of said molecular system through computer-simulated trajectories and statistical analyses [46]. In this study, molecular models were constructed at the atomic level using Materials Studio [47] (a software platform designed for conducting material simulation, computational analysis, and design). Subsequently, the choice of the COMPASS force field was determined through a comprehensive compositional analysis. The COMPASS (condensed-phase optimized molecular potentials for atomistic simulation studies) force field is a rigorously developed potential energy model  
Figure 4: Layered crystal structure of sand (0 0 1).  
Figure 5: Molecular model of sand crystal structure.  
Figure 6: Interface model of epoxy resin and sand: (**a**) DGEBA–Sand; (**b**) DGEBDS–Sand; (**c**) AEOR–Sand.  
that combines ab initio calculation and empirical data parametrization [48]. Primarily utilized in atomistic simulation studies, this force field allows for the precise optimization of molecular potentials in condensed phases. It enables accurate predictions of the structural organization, conformational dynamics, vibrational properties, and thermodynamic behaviors of both isolated and condensed molecules. Empirical evidence from numerous studies conclusively supports the use of this force field in investigations encompassing both organic and inorganic systems [46; 47]. The NVT ensemble (canonical ensemble) was used, and the system initial temperature was set at 298K. Temperature was controlled by using the Nose-Hoover method, and the simulation time was 10 ns. The attainment of an equilibrium state was observed when the total energy of the system converges. Subsequent to this, computational assessments were performed to determine the adsorption energy of the resin and soil, the interface hydrogen bonds after the adsorption, and the mechanical characteristics. To avoid trapping in a single potential well, a minimum distance of 5A was maintained between the resin and the silica surface during the initial stages of the simulation. When analyzing the adsorption energy and the hydrogen bonds of all configurations, default values were employed for the maximum hydrogen acceptor distance and minimum donor-hydrogen-acceptor angle, which were set at 2.5 A and 90\({}^{\circ}\), respectively.
## 2 Materials and Methods
### Deep Neural Network-Based Channel Estimation and Signal Detection  
#### 2.5.1 Deep Neural Network  
The architecture of the OFDM system with deep learning (DL)-based channel estimation and signal detection is illustrated in the following figure.  
Assuming that a frame is formed from the pilot symbols staying in the first OFDM block and the following OFDM blocks of the transmitted data, the channel can be changed from one frame to another, but it is to be constantly spanning over the pilot block and the data blocks. One pilot block and one data block are considered to be the received data to input of the DNN model. Then, the DNN model recovers the transmitted data in an end-to-end manner.  
Figure 3 describes the OFDM architecture system employing DL-based channel estimation and signal detection. A data bit stream is converted from the transmitted data after being inserted with pilots. Then, to change the message from the frequency domain into the time domain, the IFFT is used. Next, cyclic prefix (CP) was introduced to diminish inter-symbol interference (ISI). However, the CP size must be greater than maximum channel  
delay spread to mitigate the ISI. Let a multipath channel be represented by complex random variables \(\{h(n)\},n=[0,N-1]\). Received signal is described as \(y(n)=s(n)*h(n)+w(n)\), where \(*\) indicates circular convolution; \(s(n)\) and \(w(n)\) represent the transmitted signal and the noise, respectively. Finally, the received signal at the receiver after removing CP and performing FFT, in frequency domain, is expressed as  
\[Y(k)=S(k)H(k)+W(k), \tag{18}\]  
where \(Y(k),S(k),H(k)\), and \(W(k)\) are the DFT of \(y(n),s(n),h(n)\), and \(w(n)\), respectively.  
In Figure 3, there are two stages to obtain an effective DNN model for joint channel estimation and symbol detection. In the offline training stage, the model is trained with the received OFDM samples that are generated with different data series and under various channel conditions with certain statistical properties, or realistic channels. In the online deployment stage, the DNN model creates the output that recovers the transmitted data without clearly estimating the wireless channel [10].
## 4 Discussion
### Supergene Enrichment of REE
#### 4.1.2 Effects of Organic Matter and pH on REE Enrichment  
The TC and TOC contents in both profiles were low (Table 2). The average TC and TOC value in both profiles was less than 1%. In the topsoil, the TC and TOC were significantly high, related to the vegetation and abundant sources of organic carbon on the surface. The high values of TC and TOC at the RSI may due to the gathering of water and nutrient elements here, creating a suitable environment for microorganism growth. However, it was not observed potentially.  
The correlation between organic carbon and REEs is generally insignificant. However, with the increase in atomic radius, the hydrating ion radii of REEs increase gradually, and the coordination ability with inorganic and organic acid ions strengthens, and the migration ability increases [47]. Complexes formed by HREE with some inorganic and organic complexes, such as bicarbonate, phosphate, and humic acids, are generally much more stable than the complexes formed by LREE [48].  
The REE concentration coefficient in Figure 7 shows that the laterite profile on Vietnam carbonate rocks had strong REE enrichment ability, especially at the RSI, depicting a single peak curve for the SC profile. However, in the TG profile, two more weak peaks were observed at \(-90\) and \(-150\) cm in addition to the RSI peak, depicting a multi-peak curve (except for Ce) (Figure 7). [PERSON] et al. [32] reported that a narrow and mutated alkaline barrier formed during the formation of residual soils (early stage of weathering). This barrier can effectively facilitate REE adsorption by clay minerals to form an enrichment peak. The pH of these two profiles showed weak acidity, significantly lower than that of the crust over carbonate rocks in subtropical areas in Southern China, where the average pH is 6.2 [32]. The maximum pH values in these two profiles appeared at the RSI, but with two more peaks at \(-60\) and \(-90\) cm in TG profile, which correspond to the peak accumulation of REE  
Figure 7: The variation of REEs and pH with depth in **(A)** SC and **(B)** TG profiles.  
Studies have shown that rare earth elements (except Ce) are significantly correlated with pH, and the correlation coefficient decreases with increasing atomic coefficient [49]. In this study, the correlation coefficients between REE from La to Lu and pH ranged from 0.656 to 0.639. The correlation between LREE and pH was higher than HREE. On the one hand, the pH value directly affects the solubility of REE and changes their migration behavior. The pH value can also influence the activity of iron and manganese oxides. Within the pH range of the soil, the protons of hydroxyl groups on the surface of iron and manganese oxides are easily dissociated and are thus prone to obligate adsorption with REE [50].
# Software Defined Networks in Wireless Sensor Architectures
## 4 Emerging Sensor Networks: Software Defined Sensor Networks
### Software Defined Sensor Networks Challenges
To get these features, this architecture is layered including: an adaptation layer (packet formatting), virtualization layer (part of the network according to the terms of the topology) and a controller (working with flow table rules). Since there are obligatory data aggregations as the exchange messages between sensors and the controller when the network topology is being created, it is not clear if this architecture optimizes the energy use in sensor nodes. [PERSON] et al. propose SDN-WISE [67; 68], an extension of Sensor OpenFlow [13]. It uses SDN in order to simplify policies to reconfigure WSN that are composed of distinct vendor sensor nodes. SDN-WISE is based on multiple controllers (local controllers) and an additional one (global controller)that works as a proxy between the data plane and them. Its main feature is that sensors only participate in local control tasks, removing the interaction with the global controller. Therefore, a packet can be treated in different ways since different flow rules are programmed within controllers. Continuing along this line, [PERSON] et al. [69] propose a solution to increment WSN management. The SDN controller is composed of a five-layer stack: the lower three layers are the physical layer, the MAC layer and finally the NOS layer. The controller is placed in the fourth layer, and the fifth is the Application layer where tasks such as routing, QoS and localization are managed. The authors affirm that this layered and centralized architecture achieves better performance in management tasks, and it also offers an energy savings in sensor nodes that belong to the release of the communication load. As is stated in the previous section, the union between WSN and SDN architecture allows for multitasking in WSN. In this way, [PERSON] et al. [14; 62] propose an architecture comprised of WSN and SDN allowing multitasking sharing the same network resources. Therefore, each sensor contains several programs running within them, which belongs to these applications based on user requirements. In order to support multitasking in an efficient way, it offers an optimization (in terms of load and energy) in all applications managed by a global controller with a scheduling process. This optimization process is defined as a minimum energy sensor activation problem taking into account restrictions on storage and coverage. Table 2 gathers the SDN-based solutions for WSN and their features. The four right-side columns describe the features of each solution. A column named OpenFlow presents whether the proposed model is compatible with the OpenFlow protocol. Next, the following column is called Data Aggregation, which determines if the proposal aggregates data to the network. The management column shows if the solution manages network resources efficiently. Finally, the last column stated as Resource allocation indicates if the proposed solution is able to allocate network resources.
# Energy Management of Virtual Power Plant Considering Distributed Generation Sizing and Pricing
## 6 Simulation Results and Discussion
### Cut-Off Wind Effect  
In Table 8, the effect of cut-off wind turbine speed on the optimum size and cost of the proposed grid is shown.  
As it can be seen, while the number of wind turbines with low cut-off speed is decreased, the total cost increases by growing the cut-off speed. This indicates that more wind turbines working with low cut-off speed means less power will be generated. Therefore, other DGs have to operate at a higher power level and this raises the cost of operation. It is because the input of wind turbines is cheap wind energy, while some of those DGs use hydrogen fuel or heat as input power.
# Privacy-Preserving Feature Selection with Fully Homomorphic Encryption
## 2 Preliminaries  
### Consistency Measure  
First of all, we review the notion of the consistency measure employed in our problem. A consistency measure \(\mu:2^{F}\rightarrow[0,\infty)\) for a feature set \(F\) is a function to represent how far the data deviate from a consistent state and is required to satisfy _determinivity_ (\(\mu(F)=0\) if and only if \(F\) is consistent) and _monotonicity_ (\(F\subseteq G\) implies \(\mu(F)\geq\mu(G)\)). The following consistency measures satisfy this requirement.  
* \(\mu_{\text{bin}}(F)=0\), \(F\) is consistent; \(1\), otherwise (binary consistency [11])
* \(\mu_{\text{icr}}(F)=\sum_{x}(\Pr(F=x)-\max_{x}\Pr(F=x,C=c))\) (ICR [32])
* \(\mu_{\text{rs}}(F)=1-\sum_{c}\frac{FD_{C=c}}{|D|}\), \(FD=\{D_{F=x}\mid D_{F=x}\subseteq D\}\) (rough set [33])
* \(\mu_{\text{ie}}(F)=\sum_{x}\sum_{c\
eq c^{\prime}}\frac{|D_{F=x,C=c^{\prime}} |\cdot|D_{F=x,C=c^{\prime}}|}{|D|^{2}}\) (inconsistent pair [34])  
The fully homomorphic encryption used in this study is specialized for binary operations. Therefore, among these consistency measures, we employ \(\mu_{\text{bin}}\).
# An Optimized Planning Tool for Microwave Terrestrial and Satellite Link Design
## 3 Tool Description
### Terrestrial Links
#### 3.4.3 Radio Characteristics  
In this design step, the user inputs all the radio parameters such as transfer rate, bandwidth, modulation, and frequency. The user can analyze multiple frequencies given a minimum and maximum frequency. The user is able to choose between quadrature amplitude modulation (QAM) and phase-shift keying (PSK) with the number of symbols inputted by the user. The user may also choose the polarization of the signal, having a horizontal and vertical option.
## 1 Introduction
### Organisation of This Paper  
The remainder of this paper is organised as follows: Section 2 provides the required background. Section 2.1 presents an overview of IEEE 802.15.4/ ZigBee cluster-tree networks and Section 2.2 presents some of the most relevant related works for the development of this proposal. Section 3 defines the considered message traffic and network models. Section 4 presents a set of boundary equations that constrain the allocation of superframe durations for each cluster-head. Section 5 models the considered message transmission duration time. Section 6 presents a timing constraint for the monitoring traffic, based on the protocol constraints of IEEE 802.15.4 cluster-tree networks. Section 7 introduces the proposed superframe duration allocation schemes. Section 7.1 presents an allocation scheme based on the load imposed by the descendant nodes of each cluster-head; Section 7.2 presents an allocation scheme based on the number of descendant nodes; Section 7.3 provides an example of the use of the proposed allocation schemes. Finally, Section 8 presents a simulation assessment of the proposed allocation schemes and discussion of the results, and some conclusions and considerations about future works are presented in Section 9.