esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
Figure 6: Means of gas concentrations of samples taken from buckets of treatment B2 at two sampling points (A and B); B2 = silage treated with biological inoculant 2, HD = high density, LD = low density. [PERSON] et al. [22] used _Lactobacillus buchneri_ alone or in combination with homofermentative lactic acid bacteria as an inoculant in their study. They also found enhanced aerobic stability as also shown in the present study and reduced yeast and mould counts, increased final pH and dry matter loss, as well as increased acetic acid and 1,2-propanediol contents and decreased lactic acid content. The findings concerning acetic acid, 1,2-propanediol, and lactic acid fully correspond with the findings at hand, as shown in Table 1.  
[PERSON] et al. [17] found an improved aerobic stability for silages inoculated with _Lactobacillus plantarum_ and _Enterococcus facecium_. The higher amount of acetic acid in treatments B1 and B2, especially in the LD variation (cf. Table 1), compared to the control treatment and the chemical treatment, shows the heterogramtative character of the biological additives. Acetic acid is known for its ability to improve aerobic stability of silages [11] and can be seen as main reason for the absence of deterioration of silages treated with biological additives in the present study. According to this, [PERSON] et al. [11] also used _Lactobacillus buchneri_ in trials associated with silages high in acetic acid concentrations and found higher aerobic stability. Another parameter, which is changed by chemical inoculation is the pH (cf. Table 1). The pH is lower in the CHEM treatment than in the other treatments because there is less acid built by fermentation. The high initial CO\({}_{2}\) concentrations in all of the buckets used confirms the circumstances of ensiling to be optimal and, therefore, underpins the findings of [PERSON] [14]. [PERSON] and [PERSON] [23] also refer to high CO\({}_{2}\) concentrations as an indicator for well-sealed silage.
## 2 System Model  
As depicted in Figure 1, a typical VEC network is considered, which consists of an RSU, multiple task vehicles, and multiple service vehicles moving at constant speeds in the same direction. Computational offloading services for the vehicles are provided by the MEC server deployed on the RSU and the service vehicles that have idle computing resources. Denote \(\mathcal{TV}=\{1,2,\dots,V\}\) as the sets of the task vehicles and \(\mathcal{SV}=\{1,2,\dots,S\}\) as the sets of the service vehicles.  
Figure 1: System model.  
The system is executed in a time-slotted manner, the time is split into many time slots of equal length, and \(\tau\) represents the length of each such time slot. At the beginning of each time slot, a task with a data amount of \(D_{i}\) is randomly generated by the \(i\)-th task vehicle, \(\forall i\in\mathcal{TV}\), with a probability \(\lambda\), where \(\lambda\in[0,1]\). Each task vehicle manages a task queue. When a new task is generated, it is appended to the end of the queue. And tasks are processed in a first-in-first-out (FIFO) manner, starting from the head of the queue. Once a task is completed, it is immediately removed from the queue. The partial offloading model is considered, where the task is capable of being partitioned into two parts: one part is processed locally, and the other part is offloaded either to the RSU via a V2I link or to the neighboring service vehicles via a V2V link for processing [37]. So, we define \(\mathcal{N}=\{0,1,2,\ldots,j,\ldots,S\}\) as the set of service nodes that consists of the MEC server and service vehicles, where 0 is the MEC server and \(j\) is the \(j\)-th service node. At the start of every time slot, the task vehicles process the task at the head of the queue based on the offloading ratio \(\xi_{i}\).  
We employ orthogonal frequency-division multiple access (OFDMA) technology to allocate \(K\) subcarrier resources among multiple task vehicles, which the set denotes as \(\mathcal{K}=\{1,2,\ldots,K\}\) with each subcarrier having a bandwidth of \(B\). The subcarrier allocation matrix is denoted as \(\mathbf{W}=\left\{w_{s,i,j}\mid w_{s,i,j}\in\{0,1\},s\in\mathcal{K},i\in\mathcal{ TV},j\in\mathcal{N}\right\}\), where \(w_{s,i,0}=1\) means that the subcarrier \(s\) is allocated to the \(i\)-th task vehicle for the purpose of offloading its task to the RSU, otherwise \(w_{s,i,0}=0\). Similarly, \(w_{s,i,j}=1\) means that the subcarrier \(s\) is allocated to the \(i\)-th task vehicle to enable the offloading of its task offload the task to the \(j\)-th service node, otherwise \(w_{s,i,j}=0\).
# Hybrid Power System for the Range Extension of Security Robots: Prototyping Phase
## 5 Conclusions  
The process of prototyping an HPS for use in security robots was described. Based on the developed specifications for the HPS, the following components were acquired: fuel cells with 1 kW nominal power (Horizon Fuel Cell Technologies, H-1000W), batteries with 40 Ah nominal capacity (Samsung SDI, 18650-25R), a DC/DC converter with 960 W rated power (Mean Well, SD-1000L-24), and composite cylinders storing up to 0.71 kg of hydrogen compressed at 310 bar (Inocom, SCBA-9.0L). Unlike the other components, the fuel cell required installation as most of its parts were provided externally. Therefore, a fuel cell enclosure was developed to incorporate the separate parts, such as the controller, switches, connectors, valves, and tubes. Chillers and heaters were also installed in the enclosure to condition the air flowing into the fuel cell. An electronic load coupled to a charge-discharge system controller was used to emulate both the mechanical load required for patrolling and the electrical load required for surveillance. The HPS prototype was tested and the maximum output power that the fuel cell could sustain for 24 h was determined to be 850 W. To further increase the energy density of the HPS, the structure was converted to a plug-in hybrid featuring larger batteries and a wider SOC. Prior to testing the converted prototype, the impact of the plug-in hybrid structure was simulated. The results show that the operation time was extended by 2.07 h with a larger battery (7S12P) over the widest SOC window (90%). In consideration of the simulation results, the plug-in HPS prototype was integrated into the security robot. To emulate the mechanical load required for patrolling and apply it directly to the security robot through its tires and wheels, a chassis dynamometer is required. However, typical off-the-shelf dynamometers are not suitable because our security robot is based on a four-wheel drive and has a short wheelbase (0.8 m). Therefore, a chassis dynamometer dedicated to our security robot was developed. The integrated prototype was tested, and its capability to continuously operate the security robot for 24 h was demonstrated.  
Conceptualization, W.S.; methodology, W.S.; software, W.S.; validation, W.S. and Y.-G.P.; investigation, W.S.; writing--original draft preparation, W.S.; writing--review and editing, W.S.; visualization, W.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Korean Evaluation Institute of Industrial Technology and conducted by the Ministry of Industry and Commerce in 2017 (Industrial Core Technology Development Project, Project Number 10080489).  
The author declares no conflict of interest.  
Figure 7: Results of integration tests carried out to show a discrepancy in the amount of load between our dynamometer and an actual road.
## 5 Stability Enhancement Using FACTS Devices  
Stability is one of many problems associated with interconnected electrical networks [51]. To fulfill the generation and demand gap for electrical power, several methods and techniques have been adopted to enhance power system performance in recent years. Integration of renewable energy and smart grid interaction with existing networks are common examples [52]. Due to these changes, power system stability, reliability, and security are at risk in terms of its smooth operation. Therefore, for reliable operation of power networks, stability enhancement is a necessity in deregulated networks [53].  
Power system controls, like voltage (V) and frequency (f) controls, are ancillary services in deregulated networks. Inability to maintain these V and f controls within their established margins can lead to instability. Similarly, independent power producers (IPPs) that are merged into power networks extensively have low capability to handle stability in terms of frequency [54]. Therefore, significant measures should be needed to analyze power system behavior under frequency instability [55; 56]. Here, we will discuss both load frequency control (LFC) and voltage control, one by one, for stability enhancement in deregulated networks.
# Towards Automatic License Plate Recognition in Challenging Conditions
## 3 Methodology
### Vehicle Detection
Each RoI is expressed by a four-tuple \((r,c,h,w)\), which specifies its top-left corner (\(r,c\)) and its width (\(h,w\)). Every feature vector (\(\eta\)) is passed as an input into a sequence of fully connected (FC) layers. In between, the RoI pooling layer uses max pooling to transform the features inside a binding region into a small feature map. Moreover, only a few RPN proposals highly overlap with each other. Therefore, to reduce redundancy, we adopt non-maximum suppression (NMS) in the proposal regions. We fix the IoU threshold for NMS \(\geq\) 0.5, which leaves us about 2000 proposal regions per image with a significant decrease in the number of proposals. After the NMS, the _top-N_ ranked proposal regions are estimated to detect vehicles and draw a red rectangle around them. Once the vehicle is located in the input image, we apply our method to locate a LP within the bounding box that contains the vehicle.
# Biography of Muscle Tension Dysphonia: A Scoping Review
### History  
The origin and foundations of what is currently known as Muscle Tension Dysphonia (MTD) lie in the early 1980s. In 1982, Koufman and Blalock [5], in an attempt to better define and organize functional disorders of the voice, proposed a way to classify these disorders by dividing them into five big groups: conversion reaction, postviral chronic hoarseness, inappropriate falsetto, postoperative dysphonia, and vocal misuse/abuse syndromes. Half of the patients belonged to the group with vocal misuse or abuse syndromes (VMS). Moreover, most of the patients in the VMS group had altered skeletal laryngeal muscle tension, characterized by chronic intermittent dysphonia and vocal fatigue. On this basis, the authors decided to refer to this disorder as laryngeal tension-fatigue syndrome (TFS). This type of disorder affected, according to the authors, 25% of all functional voice disorders [5]. Moreover, the authors differentiated TFS in the voices professional and non-professional subjects. According to the authors, in the former, the tone of the voice is very low, while, in the non-professionals, the tone of the voice is variable. TFS syndrome in voice professionals was labeled as Bogart-Bacall syndrome [6].  
TFS was the predecessor of what is currently known as Muscle Tension Dysphonia (MTD), since TFS is a functional voice disorder characterized by an alteration of tone but with a normal appearance of the larynx by physical examination. The concept of TFS, however, was limited by the fact that it only applied to cases in which the origin of the dysphonia was VMS; it did not cover other possible etiological factors [1].  
The term MTD was first used in 1983 by Morrison and Rammage [7]. In their work, these authors described the clinical characteristics of young people who made excessive use of the voice under stress. As in TFS, the authors found an increase in muscle tension in MTD. They assessed this muscle tension by palpation of the extrinsic laryngeal musculature and by means of laryngeal fibroscopy when evaluating the behavior of the larynx during phonation [8]. However, the principal difference between MTD and TFS, and the reason why the new term came to be more-widely used internationally, is that MTD denotes a pathological condition in which the increased laryngeal and/or para-laryngeal muscle tension can be caused by diverse etiological factors. The newer term covers a clinical disorder that can arise from various factors and not just VMS [1].  
We chose to conduct a scoping review because our topic of interest is very broad, and this type of study allows us to systematically summarize findings and identify gaps in the literature among studies with heterogeneous methods. This scoping review was guided by the following questions: (1) What is the historical origin and background of what we know today as MTD? (2) How is the diagnosis of MTD made, and what tools do we currently have in daily clinical practice? (3) What are the actual treatment options for MTD?
## 3 Results  
### Plant Parameter Estimation for Model-Following Benchmark  
Dynamic systems can often be modeled in terms of a finite set of parameters that can be methodically estimated through minimization of the summed squared difference between a fitted model and truth model. Batch least squares (LS), recursive least squares (RLS), and extended least squares (ELS) are examples of such estimation methods. RLS incorporates an operator for multiplication with previous parameter estimates to use error between true values and estimates as a technique to update the parameter vector to its most likely state for a specific timestep (determined by the specified discretization). Discretization timestep will prove to be a very important facet of implementation. As an extension of RLS, ELS is identical to RLS, where the system vector \(\phi\) holds values of process input and output. Using ELS, values for noise, which may be correlated, are included in the vector \(\phi\). Such a structure is important when a process has naturally occurring noise, resulting in the need for an estimation of contribution to the system from noise to estimate process parameter values more accurately. The third technique tested, known as Batch LS, allows for a least squares estimation of process parameters based on a collected amount of observed input and output data, minimizing error for all observations rather than one at a time.  
Based on the formulation of the RLS, ELS, and Batch LS parameter estimation techniques, a simulation was conducted to determine estimation performance of each approach. Plots in Figure 5 depict the convergence of each estimation method towards true values for two of four total unknown parameters over the 200 timestep simulation.  
Figure 5: (**a**) Estimation of parameter \(\mathbf{a_{1}}\) on ordinate with time in seconds on the abscissa. (**b**) Estimation of parameter \(\mathbf{b_{0}}\) on ordinate with time in seconds on the abscissa. (**c**) Estimation of parameter \(\mathbf{a_{2}}\) on ordinate with time in seconds on the abscissa. (**d**) estimation of parameter \(\mathbf{b_{1}}\) on ordinate with time in seconds on the abscissa.  
As shown in Figure 5 and Table 1, estimation using ELS performed the same or better than implementations of RLS and Batch LS. ELS had an error 9% higher than RLS for estimation of \(\mathbf{a_{1}}\) but performed better in estimating the parameters of \(\mathbf{a_{2}}\), \(\mathbf{b_{0}}\), \(\mathbf{b_{1}}\) with equal or lesser error averages and standard deviations than ELS and Batch LS. RLS performed similarly to ELS in estimating parameters \(\mathbf{a_{1}}\) and \(\mathbf{a_{2}}\), and Batch LS exhibited high error in comparison to RLS and ELS.
## 4 Discussion
### Direct and Indirect Methods to Evaluate Degree of Conversion (DC)  
The degree of conversion (DC) can be assessed using direct methods such as Fourier-transform infrared (FTIR) spectroscopy [9,43,48] and Raman spectroscopy [8,31], which measure the conversion of carbon double bonds to single bonds in the resin matrix. Indirect methods like the scrape-back length technique according to ISO 4049 [49,50], which has been criticized for overestimating DoC, and microhardness testing in terms of bottom-to-top microhardness ratio [19,51,52,53] are also widely employed. In the present study, the DoC was evaluated through the bottom-to-top microhardness ratio. Recent studies continue to support the correlation between microhardness and DoC, noting that a bottom-to-top hardness ratio of 0.80 correlates with a DoC of 0.90 for non-bulk-fill composites, as demonstrated by [PERSON] [45] and [PERSON] et al. [54] and confirmed by modern investigations [9,48]. However, it is critical to recognize that raw microhardness values cannot be directly compared across different composites due to variations in filler type, size, and resin matrix composition [43]. The DoC typically shows a more rapid decline with increasing depth compared to microhardness, highlighting the need for appropriate curing protocols [45,52,54]. [PERSON] et al. [31] compared the DoC of the SDR bulk-fill by a polywave and monowave LCU at a 4 mm depth through Raman spectroscopy; no significant difference was found between the polywave and several monowave LCUs used. The advantage of direct methods is that they give a percentage of reacted double bonds, and the disadvantage is that they can be measured in unreacted and reacted composites, while the microhardness ratio used in this study can be measured at different depths of the same specimen [8,11,12,13,17,18,48,50,52,53,54,55,56,57,58]. The microhardness ratio depicts the deterioration of the mechanical properties of a composite resin at a certain depth compared to the surface and could give some idea about clinical performance. An enhanced DoC was reported when irradiation was extended to 40 s [8,28,31,52,53].
## 1 Introduction
Given image \(I\), _[PERSON]_ would like to determine whether there are images in _[PERSON]'s_ collection \(D\) that are similar to \(I\) (e.g., duplicate, near duplicate, somewhat close, etc.) without disclosing either \(I\) or \(D\). We focus primarily on security, where protecting the descriptors of images is necessary. Specifically, our proposed scheme supports speeded up robust features (SURF) local-feature [6; 7] with cosine similarity [8], as well-known metric to score matching images, and employs homomorphic encryption [9] to protect the confidentiality of descriptors. To increase the effectiveness of the image search, we build a tree-based index structure by employing the \(k\)-means clustering algorithm to classify large-scale images database into various clusters. The method allows only the inquiring side to see the matching value. Hence, only _[PERSON]_ is interested in determining whether she has any image in common with _[PERSON]_, without worrying about the leakage of unnecessary information. The contributions of this paper are as follows. First, a trivial solution to achieve secure and private image matching is to utilize a trusted third party (TTP). _[PERSON]_ sends \(I\) to the TTP and _[PERSON]_ sends \(D\) to the TTP, and then TTP can investigate and inform _[PERSON]_ whether images similar to \(I\) can be found in _[PERSON]'s_ collection. However, in real life situations, finding a completely trustworthy third party is a difficult task. Our work does not require such a third party. Second, the applications of PIM often suffer from significant overhead for the image encryption operation. Our scheme can work without image encryption and still maintain the privacy of the parties involved. Third, in SEPIM, the duration of an enquiry and its accuracy are comparable with enquiries conducted using plaintext search methods. Finally, for communication cost, our scheme only requires one round of communication between a query side and a data owner side, while others need multiple rounds of communication. The rest of this paper is organized as follows. Related works are reviewed and discussed in Section 2. Section 3 introduces the preliminary techniques. Section 4 introduces the security requirements and the problem definition. Section 5 provides the detail construction of SEPIM. The evaluation of SEPIM is provided in Section 6, and conclusions and future works are drawn in Section 7.
# Measurements of Entropic Uncertainty Relations in Neutron Optics
## 3 Measurement of Entropic Noise-Noise Uncertainty Relation
### Set-Up
At \(q=1\) the neutrons, as indicated on the right of Figure 4, propagate along the y-axis with \(|+z\rangle\) and are transformed by DC-Coil 2 to the eigenstates \(|\pm a\rangle\), respectively \(|\pm b\rangle\). The entropic concept of the noise demands that the two eigenstates are initially unknown and equally likely \(p(a)=p(b)=1/2\). For this purpose, the coils are driven by a power supply whose current is controlled by a random number generator (RG) operated by a computer. This RG will produce the desired eigenstates with equal probability distribution at random. In DC-Coil 3 the current is adjusted to make an analysis of the eigenstates in the direction \(\vec{r}_{1}(\theta_{1},\phi_{1})\), i.e., the probability to pass the Analyzer 2 before the detector is \(\frac{1}{2}(1+\vec{a}\cdot\vec{r}_{1})\) (likewise for \(\vec{b}\)). The angle \(\theta_{1}\) is controlled once more by the current in the coil while the azimuthal angle \(\phi_{1}\) is a function of the distance between DC-Coil 2 and DC-Coil 3. In the case \(0<q<1\), the pink-colored section in Figure 4 is integrated into the setup. The incoming neutron is set to pass to the second stage of the instrument with probability \(q=|\langle+z|\psi\rangle|^{2}\), where \(|\psi\rangle\) is generated according to Equation (20) by DC-Coil 1. Then, as usual, the second coil produces one of the eigenstates of \(A,B\) at random before analysis in direction \(\vec{r}_{1}\). After some time, however, the incoming state \(|\psi\rangle\) is inverted and changes the probability of passing to \(1-q=|\langle+z|-\psi\rangle|^{2}\). If that is the case, DC-Coil 3 is conditioned to make a measurement along \(\vec{r}_{2}\). At the end of the beamline, a count rate of about 40 neutrons per second remains, whereby all particles are registered in the detector independently of the spin. The efficiency of the detector is close to 1 owing to the high absorption cross-section of boron for thermal neutrons. The statistics of the counting rate follows a Poissonian distribution, hence the error of one standard deviation is equal to the mean value. The largest systematic error stems from the limited efficiency of resolving the polarization at the supermirror analyzers which causes a loss of state fidelity from 1 to approx. 0.98. To deal with the error, the insufficiency is compensated by marginally rescaling the values such that they cover the full range from 0 to 1.