esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Further, a computational study reported that applying anodal stimulation on the C1 and cathodal stimulation on the C2 (i.e., the C1-C2 montage) resulted in higher electrical field intensities for both hemispheres, reaching deeper regions of the lower limb motor area [20]. These findings suggested that the C1-C2 montage may be suitable for improving lower limb functions such as walking by balancing interhemispheric activity [50]. In fact, our subsequent analyses that examined potential interlimb force control asymmetry confirmed that no significant differences in mean force, force variability and force regularity between feet were observed across two tDCS conditions. Taken together, our findings provide evidence that the C1-C2 montage would be an additional option for tDCS-based lower limb rehabilitation programs. Despite positive effects of tDCS on bilateral ankle-dorsiflexion force control, these findings should be cautiously interpreted. First, the current study focused on healthy young adults. Given that neuroplasticity in the motor cortex may be influenced by age or clinical characteristics [51], future studies should investigate effects of bilateral tDCS protocol with the C1-C2 montage on lower limb motor functions in older adults or patients with neurological disease such as Parkinson's disease and stroke. Second, goniometric data of the lower limbs were not measured in this study despite the potential relationship between ankle-dorsiflexion mobility and functional performance in the lower limbs [52; 53]. Given that tDCS on the sensorimotor cortex would improve the range of motion of hip, future studies should examine tDCS effects on lower limb motor functions based on bilateral ankle joint mobility. Third, improved bilateral ankle-dorsiflexion force control performance may not necessarily lead to improvements in gross motor functions of lower extremities. However, considering the significant relationship between force control performances and motor functions in the literature [54; 55; 56], future studies should investigate effects of tDCS protocol with the C1-C2 montage on gross motor functions in lower extremities such as postural control and locomotion. Finally, we did not measure neurophysiological changes by tDCS protocols while performing bilateral ankle-dorsiflexion force control tasks. Future studies should examine cortical excitatory patterns across crucial brain areas induced by bilateral tDCS protocol with the C1-C2 montage and how these alterations improve bilateral force control in lower extremities.
## 2 Problem Description and Assumptions
### Control Model Formulation
#### 2.3.3 Optimization Problem  
The main feature of the proposed model was the joint optimization of the control parameters \((Z,A_{sub},A_{o})\), which were necessary to optimally control the subcontracting contribution, regulate the inventory level, and define the critical age to conduct major maintenance. These control parameters must be jointly optimized to minimize the total incurred cost \(TC\). In our formulation, the total cost consisted of the sum of the inventory cost and the backlog cost \(IB(t)\), the quality cost \(QC(t)\), and the maintenance cost \(MC(t)\). Regarding the inventory-backlog cost, this component also comprised the production cost \(C_{pro}\) and the subcontracting cost \(C_{sub}\), defined as  
\[IB(t)=\frac{1}{T}\cdot\int_{0}^{T}(C^{+}x^{+}(t)+C^{-}x^{-}(t)+C_{pro}u_{1}(t) +C_{sub}u_{2}(t))dt \tag{13}\]  
where \(x^{+}=\max(0,x)\) and \(x^{-}=\max(-x,0)\) and the constants \(C^{+}\) and \(C^{-}\) serve to penalize inventory and shortages, respectively. The indicator \(QC(t)\) represents the average cost of quality per unit of time in the period \([0,T]\); it is determined by the cost of defects \(C_{def}\) and is given by the following equation:  
\[QC(t)=\frac{1}{T}\cdot\left(C_{def}\int_{0}^{T}(\beta(t)\cdot d)dt\right) \tag{14}\]  
Further, \(MC(t)\) denotes the average maintenance cost per unit of time in the period \([0,T]\) and it takes into account the cost of minimal repair and the cost of major maintenance; it is defined as:  
\[MC(t)=\frac{1}{T}\cdot\left(C_{R}\cdot N_{R}(t)+C_{M}\cdot N_{M}(t)\right) \tag{15}\]  
where \(N_{R}(t)\) and \(N_{M}(t)\) define the number of minimal repairs and the number of major maintenance performed in the period \([0,T]\), respectively. Summing up, Equations (1)-(15) led to defining the next stochastic optimization model:  
\[\left\{\begin{array}{cl}Min~{}TC(Z_{0},A_{sub},A_{o})=\lim_{t\to\infty}(IB( t)+QC(t)+MC(t))\\ subject~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~  
Another important aspect was the fact that this optimization model implied a nonlinear and stochastic problem; for this reason, traditional mathematical methods could not be applied to solve this kind of problem since it was difficult to determine an analytical solution for the total cost. The difficulty was due to the random transitions, the Markov dynamics for internal production and subcontracting, and mainly because of the gradual growth in the number of defects caused by deterioration. One way to overcome these difficulties was with the implementation of a simulation-optimization approach. In the next section, we detail how we determined a solution to this model and determined the value of the parameters of the control rule in a reasonable amount of time and with acceptable accuracy.
## 1 Introduction  
Dental caries are one of the most common chronic infectious diseases affecting the human population [1; 2; 3]. According to the Third National Oral Health Survey, the prevalence of caries in five-year-old children is 66% in China [4]. Therefore, studies on the prediction and prevention of dental caries in China are essential and urgent.  
_Streptococcus mutans_ (_S. mutans_), a Gram-positive bacteria and one of the main clinical pathogens responsible for dental caries, can produce acidic end products through the fermentation of dietary carbohydrates [5]. Enamel demineralization, the first step in the formation of dental caries, occurs when the pH of the local micro-ecological environment is reduced to 5.5 [6]. However, _S. mutans_ can survive for a long time at pH 5.5 because of its acid tolerance, which could result in the demineralization of dentine and the formation of a carious lesion [7]. Consequently, the cariogenicity of _S. mutans_ is closely related to its acid tolerance. The reported virulence genes of _S. mutans_ that are crucial for its acid tolerance include _ltrA_, _dnaK_, _groEL_, _uvrA_, _fft_, _brpA_, _recA_, _relA_, _glnQ_, and _glnM_[5; 8; 9; 10; 11; 12; 13; 14; 15].  
In recent years, an increasing number of studies have shown that the expression of virulence genes can be regulated by small non-coding RNAs (sRNAs) at the post-transcriptional level in bacteria. sRNAs are usually smaller than 300 nucleotides (nt) and can activate or inhibit virulence gene expression via base pairing with the target mRNAs of protein-coding genes [16; 17; 18]. The expression levels of sRNAs are altered in response to environmental changes such as acidic pH [19; 20]. To date, a large number of sRNAs have been identified in numerous bacterial species including _Staphylococcus aureus_, _Streptococcus pneumoniae_, _Escherichia coli_, _Salmonella_, _Listeria monocytogenes_, _Yersinia pestis_, _Bruella melitensis_, _Acinetobacter baumanni_, _Coxiella burenti_, and _Mycobacterium tuberculosis_[21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31]. However, the study of sRNAs in _S. mutans_ remains in its early stages. More than 900 possible miRNA-size small RNAs (msRNAs) were recently reported, but it remains unclear whether the sRNAs with small size in _S. mutans_ regulate virulence genes under acid stress conditions [32].  
Therefore, in this study, we aimed to identify sRNAs with small size (18-50 nt) in _S. mutans_ induced under acid stress through a deep-sequencing approach followed by qRT-PCR verification. We hypothesize that these sRNAs may play an important role in acid tolerance. To better explore this question, two of the most expressed sRNAs (_srn_884837 and _srn_133480), as verified by qRT-PCR, were used for further study. The expression levels of the two sRNAs and five corresponding target genes related to acid tolerance under different acid stress conditions at different time points were evaluated.
# Data Exfiltration through Electromagnetic Covert Channel of Wired Industrial Control Systems
## 2 Related Work
### Electromagnetic Side Channels  
EM side channels are the most common and widely known side channels in the literature. In 1985, a group of researchers demonstrated that EM emissions from VGA cables can be captured and used to reconstruct the visual content [18]. [PERSON] et al. [14] demonstrated the use of covert magnetic signals to leak data from remote, air-gapped computers to neighbouring smartphones. The suggested covert channel still functions even if a smartphone is stored in a Faraday shielding case. Malware regulates workloads on CPU cores to control the magnetic fields emitted by the running machine. This magnetic field can encode and transfer encryption keys, passwords, and keylogging data.  
[PERSON] et al. [12] explored the EM radiation of wired networks as a side-channel. The authors performed an analysis on the 10 BaseT Ethernet standard--capturing emissions radiated from Ethernet cables, and subsequently, decoded frames from captured radiation. The authors used a USRP x300 software-defined radio (SDR) for capturing the radiation. A hardware setup similar to their work is used for the research presented as part of this paper. Following a similar approach, [PERSON] et al. [10] used an emission from a DRAM clock to transmit information from a computer. This approach used a SDR with a log periodic  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Covert Channel** & **Type** & **Wall-Penetrating** & **Bandwidth** \\ \hline _BitWhisper_[13] & Thermal & No & 0.002 bps \\ _Fansmitter_[7] & Acoustic & No & 0.25 bps \\ _Magneto_[14] & Magnetic & No & 5 bps \\ _Odini_[15] & Magnetic & No & 40 bps \\ _USBee_[16] & Electromagnetic & Yes & 640 bps \\ _PowerHammer_[8] & Power & No & 1000 bps \\ _xLED_[9] & Optical & No & 1000 bps \\ _Hard Drive LED_[17] & Optical & No & 4000 bps \\ _Bitjabber_[10] & Electromagnetic & Yes & 300,000 bps \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of existing physical covert channels.  
antenna to capture the covert signals. They achieved a high transmission rate up to 300,000 bps with a shallow error rate of less than 1%.
## 5 Experiments and Results
### Experimental Results on Offhand Scenarios  
To assess the scalability of the INSTEAD system, additional runs with an increasing number of concurrent commuters were performed during the BCT days and without the restrictions on paths and speed required by the two sensors. In this case, the system was running as usual while MIC and EXTRAS sensors were simply not operational. In particular, the volunteer commuters were dressed normally and, for each run, they started their walk at the init line and ended at the exit line.  
Table 1 presents the results of these group runs, in temporal order of execution in different days, where the commuters were successfully detected by the C&C based on the messages from the 2D Video. In particular, the data refer to the time employed by groups of different sizes to traverse the video monitored area, accessing at the same time (crossing the init line with a few seconds of difference). These experiments are useful to demonstrate the scalability of the C&C software in processing concurrent messages and meet the real time requirement. By a comparison with the average results of an official BCT run, where the duration was influenced by the time constraints of the sensors for detection and processing, higher speed and larger variations within groups were also experimented.  
The first three runs were executed during the BCT experimental period, while the fourth and fifth were executed during the subsequent two days where the system was presented and demonstrated in front of, and involving, various types of stakeholders such as project collaborators and guests.  
The real time performance is demonstrated by a successful video tracking hand-over to the 3D Video (Figure 14), triggered by the C&C. In the BCT test runs, the 3D Video has achieved 100% success rate in tracking. Furthermore, the BCT results show that the 3D Video is capable to track and compute the hand-over and predictions for at least 25 concurrent persons. The 3D Video capacity limit comes from the raw computational performance of the deployed computation hardware platform.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Group Composition (\# Commuters)** & **Average Duration (s)** & **Duration Value Range (s)** \\ \hline
9 & 23 & 17–30 \\
8 & 23 & 21–26 \\
9 & 29 & 27–38 \\
12 & 24 & 7–34 \\
13 & 29 & 26–34 \\
4—BCT run & 37 & 28–47 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Groups of commuters detected by the C&C and indication of the time employed by them to cross the monitored area.
## 2 Materials and Methods  
### Site Description  
The study crop (alfalfa and maize) fields were in the Hopewell Creek Watershed near Mayhill, Ontario, Canada (Figure 1A). The maize site (43.525\({}^{\circ}\) N, 80.425\({}^{\circ}\) W) was located 4.4 km WSW of the alfalfa site (43.549\({}^{\circ}\) N, 80.381\({}^{\circ}\) W). The elevation at the maize site (Figure 1C ranged from 327.9 to 333.8 m asl, while the elevation at the alfalfa site (Figure 1B) ranged from 330.6 to 332.1 m asl. Thirty year (1981-2010) monthly mean average temperatures for this area (Waterloo Wellington A, Waterloo, ON, Canada, Climate ID:6149387, 9.8 and 11.1 km from study sites) between May and September, ranged from 12.5 to 20.5 \({}^{\circ}\)C with an average precipitation of 435.0 mm (Government of Canada, 2019). Both sites had silt loam soils with porosities and standard deviations of 0.46 \(\pm\) 0.05 (alfalfa) and 0.44 \(\pm\) 0.06 (maize), and bulk densities and standard deviations of 1.36 \(\pm\) 0.09 g cm\({}^{-3}\) (alfalfa) and 1.43 \(\pm\) 0.11 g cm\({}^{-3}\) (maize), and are classified as Humic Gleysols [32]. Both fields were tile-drained at depths of 90-100 cm below the ground surface and crops were not irrigated. Originally, alfalfa was randomly seeded with nurse crops (oats, tritticale, and peas) in April 2016. In July 2016, the field was cut and only alfalfa continued to grow thereafter. The alfalfa received 0-12-44 NPK granular fertilizer at 390 kg ha\({}^{-1}\) in September of 2017. Maize was planted in rows with 80 cm spacing in early May (2018) after the field received one pass of vertical conservation tillage. The maize field was fertilized with liquid dairy manure with 45.4 kg nitrogen, 27.2 kg phosphorus and 16.5 kg potassium dry fertilizer. Starter fertilizer was also applied at 0.05 m\({}^{-3}\) ha\({}^{-1}\). During the 2018 growing season, sprouting of alfalfa was observed shortly after snowmelt (late April) while maize sprouting was not observed until 29 May.  
Maize (_Za mays_) and alfalfa (_Medicago sativa_) crops were used as they are two of the most prominent forage crops in the study region in Southern Ontario, Canada (Great Lakes Region). Differences between these two crops are highlighted by physiological aspects including photosynthetic pathway and growth patterns, as well as farming practices that influence plant growth. Alfalfa is an herbaceous perennial legume that follows the C3 pathway of photosynthesis and is an important protein source [33]. Typical farming practices in southern Ontario include random summer seeding in the year prior to harvest, which results in an equally distributed canopy, undisturbed growth until flowering occurs, and, harvesting before flowers develop. Multiple harvests are made in one growing season. In contrast, maize is an annual crop that follows the C4 pathway of photosynthesis [34]. Maize is planted in rows, resulting in linear canopy growth and increased soil exposure prior to canopy closure. Maize is harvested once at the end of the growing season at the start of which it is planted.
# Optimization Design for Sparse Planar Array in Satellite Communications
## 4 Sparse Planar Array Optimization Design Method
### Intelligent Optimization Methods
#### 4.2.3 Local Optimization Methods
Aiming at the problem of pattern reconstruction, a pattern reconstruction method based on compressed sensing theory was proposed in [88], which realizes multi-beam reconstruction with an appropriate excitation coefficient and minimizes the number of elements. Aiming at large arrays, a pattern reconstruction method based on multi-task Bayesian compressed sensing was proposed according to the stimulus and merging of small-pitch elements [89]; on the basis of ensuring the accuracy of the reconstruction pattern, a sparse solution with a small number of elements is obtained in a short time. Aiming at the beamforming problem, a rectangular sparse area array synthesis method based on compressed sensing theory was proposed in [90], which can quickly and effectively shape the beam map of interest and obtain a smaller number of elements. The authors of [91] proposed a beamforming method based on convex optimization and MT-BCS to realize controllable secondary lobe-level reference shape beamforming, and optimized the number of elements and layout. The local optimization method is suitable for solving the problem of additional constraints, and this kind of method has strong applicability and can maintain high computational efficiency. In recent years, the method based on compressed sensing theory has shown many excellent properties, such as effectively alleviating sampling pressure, reducing storage, processing, and transmission costs, and exhibiting high matching accuracy; however, due to the grid mismatch problem [92], it still has certain limitations in sparsity rate. If there is a breakthrough in this aspect, improvements can be made in the optimization design method of sparse area arrays.
# Machine Learning for Evaluating Hospital Mobility: An Italian Case Study
## 3 Materials and Methods
### Data Entvelopment Analysis  
Data envelopment analysis (DEA) is a boundary-based optimization model that allows for the assessment of operational units by comparing their performance. It is particularly useful in the healthcare sector for measuring the effectiveness with which hospitals use available resources to maximize outcomes in terms of services provided. DEA allows for the examination of efficiency variations over time and the assessment of the impact of incentivization policies on hospital performance, highlighting that such reforms can significantly influence operational efficiency [30].  
In our study on the analysis of healthcare mobility in the Apulia-Emilia-Romagna macroregion, we applied an output-oriented data envelopment analysis (DEA) to evaluate the efficiency of the 127 hospital facilities in the region. This analysis aims to investigate mobility at the hospital level and within the public or private network. In this analysis, we considered kilometric mobility as the output and the eight healthcare characteristics identified in our predictive model as inputs. To ensure a weighted assessment of healthcare characteristics, we used the impact scores from our model as weights during output calculation. The main goal of the analysis was to maximize the total output of hospital facilities while considering the relative efficiency of each facility in influencing patient mobility in the region. This approach allowed us to identify facilities that operate efficiently in terms of patient mobility and understand the dynamics that determine their effectiveness in facilitating patient movement within the region. The relative efficiency of each hospital facility was calculated using the following formula:  
\[ER_{i}=Max\frac{\sum_{i=1}^{N}\lambda_{i}\cdot y_{i}}{\sum_{i=1}^{N}\lambda_{i }\cdot x_{i}} \tag{10}\]  
Subject to  
\[\begin{array}{l}\sum_{i=1}^{n}\lambda_{i}\cdot x_{i}\leq\ x_{j}^{*},\ \ \ \ \ \ j=1,2,\ldots,m\\ \lambda_{i}\leq\ 0,\ \ j=1,2,\ldots,n\end{array} \tag{11}\]  
where \(y_{i}\) represents the output (kilometric mobility) of facility \(i\) for characteristic \(j\); \(\lambda_{i}\) represents the weights (the impact scores) assigned to healthcare features, normalized to  
Figure 4: Confusion matrix.  
the range between 0 and 1; \(N\) represents the total number of hospital facilities; \(x_{ij}\) is the value of input j for decision entity \(i\). \(x_{j}^{*}\) is the maximum value of input j among all decision entities. The results of the DEA analysis will range between 0 and 1, indicating the degree of relative efficiency of each hospital facility.
## 3 Materials and Methods
### Summary Approach  
The algorithm requires a series of preprocessing and processing steps in order to cluster and distinguish the beats of supraventricular and ventricular origin. Figure 2 provides a high-level illustration of the processing pipeline divided into stages. In the first stage, Data extraction and cleaning, the patient signal is preprocessed and every beat of each record is sliced to form a matrix to work on. In stage 2, Clustering and descriptors, the clustering is performed and a set of different descriptors are computed to characterize each cluster (the expanded version of this stage is shown in Figure 3). The labeling processing is in stage 3, Classification, where the descriptors are used to compute a series of proportions for each cluster, and if these proportions reach a certain threshold, the cluster is classified as SVBo; otherwise, it is classified as VBo.  
Three important assumptions for the algorithm are as follows:  
1. With a significant number of VBo, the clustering algorithm is able to separate the signals;
2. If a trend is computed on the R-R interval signal, almost all the beats above this trend are going to be SVBo and most of the VBo occur below the trend, along with the rest of SVBo;
3. In the feature space, the SVBo signals are grouped together because they share similar waveform shapes, and the VBo signals are spread all over the feature space.  
These assumptions were used to compute the descriptors and subsequently the proportions for the labeling of the clusters.