esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Server Design
#### 2.1.1 Combined Server Design  
A combined server design combines both the DB and application (AP) logical servers to support the analytics workstation on 1 server. Figure 1 illustrates a case in which all OMOF CDM environments were built with 1 physical system in a tertiary hospital in Korea. Because the institution in Figure 1 supported only one physical server, a combined server design was adopted in which both the DB (Figure 1a) and AP (Figure 1b) servers were built into 1 physical server. Figure 1 suggests that there is a single firewall between the physical server where both DB and AP servers are built and the analytics workstation, an environment where analysts can analyze. The firewalls were configured to allow data traffic from a specific Internet protocol (IP) addressed in the whitelist.  
The advantage of the combined server design is that it is simpler to build than the separated server design; hence, it can be easily introduced in hospitals with insufficient resources, such as human and computing resources. In addition, because the AP and DB servers are included in 1 system, they can communicate with each other via localhost rather than external IP; hence, they may not be influenced by the external server's infrastructure, such as bandwidth provision.  
However, because the DB and AP servers are in 1 system, the overall performance may be lower than that of the separated server design, as the 2 servers share and compete for resources within one server. Moreover, because medical data are frequently generated and added in large amounts, it is often necessary to load new data into the DB, which makes DB management cumbersome. For example, whenever a large amount of data needs to be loaded on the DB, the DB configuration also needs to be modified, such that the DB server can maximize the physical server's resources, thereby prohibiting users from using the analytics workstation. Moreover, the more frequent the number of data additions, the more complicated the DB management because the configuration tuning process must be repeated. In addition, because the DB and the AP servers are within the same physical  
Figure 1: Combined Server Design for OHDSI OMOP CDM.  
server, a stability problem emerges, such that the combined server cannot be used if either server is attacked or stopped because of overload.
# A CNN Regression Approach to Mobile Robot Localization Using Omnidirectional Images
## 2 Convolutional Neural Networks
### CNNs for Localization  
As mentioned before, autonomous robots are those capable of recognizing the environment and moving through it. In this context, localization is an essential problem that mobile robots should solve. The localization task consists in estimating the position and orientation of the robot in the environment. To achieve this, a model of the environment is needed. Regarding the modeling of the scene, different solutions can be found in the literature. Some examples use local features such as [40] that proposes a tracking method for mobile robot navigation in natural environments. Particularly, they use ORB (Oriented FAST and rotated BRIEF) and CenSurE (Center Surround Extremas) for feature extraction and SURF (Speeded-Up Robust Features), ORB, and FREAK (Fast Retina Keypoint) for feature description. Other authors propose the use of global-appearance descriptors that consider the whole input image instead of only local information. In this sense, Ref. [41] presents a comparative analysis of some global-appearance descriptors used for mapping.  
CNNs also offer a solution to the localization of mobile robots. Particularly, we can find many works with successful results using visual information to solve these tasks using CNNs. For instance, [PERSON] et al. [42] propose a CNN to process information from a monocular camera and develop an accurate robot relocalization in environments where the use of GPS is not possible. [PERSON] et al. [43] propose the use of CNN-based descriptors to create hierarchical visual models for mobile robot localization. More recently, [PERSON] et al. [44] propose a novel multi-sensor-based indoor global localization system integrating visual localization aided by CNN-based image retrieval with a Monte Carlo probabilistic localization approach. [PERSON] et al. [45] propose a CNN to build a semantic map. Concretely, they use the network for object detection in images and, then, the results are integrated in a geometric map of the environment.
## 1 Introduction
When it is applied to fluctuating power devices, the modelled device has to be kept at constant operating power. The above requirements indicate that HAMM can only be conducted under laboratory conditions with fully controllable supply conditions and device operating powers, and this further hinders the practical applications of HAMM for the harmonic source modelling of PV systems. Accordingly, the main objective of this paper is to propose a practical HAM derivation approach for fluctuating p0 ower PV systems operating under nonideal grid conditions. This paper first presents the mathematical equation behind HAMM and analyses its conventional parameter derivation procedure. Later, the harmonic generation mechanisms of single-phase residential PV systems are analytically investigated, with a special focus on the impact of supply voltage distortions and device operating powers on the harmonic current emission levels. Afterwards, comprehensive laboratory tests are performed on two different single-phase PV systems for evaluating their HAM characteristics at different power levels. Based on the correlations between HAMs of different powers, a practical  
Figure 4: The circuit representation of three types of frequency-domain models: (**a**) CCSM; (**b**) HNM; (**c**) HAMM. implementation approach for HAM derivation is proposed, with its performance compared to CCSM and HNM. Finally, to evaluate the impact of model type selection on the harmonic penetration analysis of PV systems, the European low voltage test feeder is simulated, while the integrated PV systems are represented by CCSM, HNM and HAMM, respectively.
###### Abstract  
Inductive power transfer (IPT) systems have been extensively studied and incorporated into various industrial applications. An IPT system with the capability to provide constant current output (CCO) and constant voltage output (CVO) is necessary to ensure optimal battery performance. However, the resistance of the battery will continue to increase during the charging process, making it difficult for the system to realize load-independent CCO and CVO with zero phase angle (ZPA) operation. Therefore, this paper proposes an LC/S compensated IPT system, which can provide CCO and CVO at fixed frequencies. ZPA operation can be maintained in both CC and CV modes for low reactive power loss. Moreover, the total number of compensation elements of the system is minimized, with only one compensation capacitor on the receiving side, which ensures not only the low cost of the system but also a compact receiver. Finally, the correctness and feasibility of the proposed system are verified with simulations and experiments.  
inductive power transfer; constant current; constant voltage; zero phase angle +
Footnote †: journal: Applied Sciences
# Can Social Agents Efficiently Perform in Automated Negotiation?
## 2 Negotiation Setting
### Utility Functions and Reservation Utility  
In principle, this social agent is designed to negotiate in domains with linear additive utility functions. In this setting, the negotiation scenario consists of \(\mathcal{I}=\{1,2,\ldots,n\}\) negotiation issues (or attributes) in which the domain values are represented by \(\{D_{1},\ldots,D_{n}\}\). An offer is represented by \(o\), while \(\mathcal{O}^{*}\) represents the set of all possible offers in the negotiation domain. The agents' preferences are represented by means of linear additive utility functions in the following form:  
\[\mathcal{U}(o)=\sum_{i\in\mathcal{I}}w_{i}\times V_{i}(o_{i}) \tag{1}\]  
where \(w_{i}\) represents the importance of the negotiation issue \(i\) for the agent, \(o_{i}\) represents the value for issue \(i\) in offer \(o\), and \(V_{i}(.)\) is the valuation function for issue \(i\), which returns the desirability of the issue value. Without losing generality, it is assumed that \(\sum_{i\in\mathcal{I}}w_{i}=1\) and the domain of \(V_{i}(.)\) is in the range of [0,1] for any \(i\).  
This assumption is taken by the opponent modeling mechanism employed by the agent, as the agent attempts to learn each \(w_{i}\) and \(V_{i}\) for the opponent's utility function. However, as the reader also observes, the rest of the agent's components (e.g., concession tactic, bidding strategies, etc.) are independent of the learning mechanism employed by the agent. That is, one can think of the opponent modeling mechanism as an interchangeable module or service that provides information to the agent about the utility provided by an offer to the opponent. This is similar to the philosophy followed by the BOA framework [32], which allows agent designers to implement their bidding strategy, opponent modeling, and accepting strategy separately so that the performance of any components can be compared with the others by keeping other components the same. Therefore, the agent could easily be adapted to other domains with other types of utility functions [33].  
We also assume that the utility functions are non-discounted. That means that offers do not lose value as the negotiation process unfolds. We believe that discounted domains do not represent a notable majority of the negotiation scenarios. In any case, without any loss of generality, the social component of our agent would only require small adjustments to be employed in such domains.  
In the experiments that we carried out, we assume that the reservation value is zero. That is, if a negotiation fails, agents do not obtain any value from the negotiation. This decision is employed to ensure compatibility with some of the state-of-the-art opponents selected in the experiments section. Our agent can work in both domains with and without reservation utility by filtering out those offers that are not above the reservation utility and capping concession.
## 3 Preliminaries: Network Model and Problem Definition
### Neural Network Structure Used by TOMAC-PPO  
In order to enable intelligent agents to have a certain degree of memory and solve the local observation problem in network models, the TOMAC-PPO algorithm proposed in this paper combines the Transformer model with a fully connected network. The strategy network structure used by TOMAC-PPO is shown in Figure 7. The network input is state \(s\), and the output is action probability density \(\pi(a|s;\mathbf{\theta}^{i})\), where \(\mathbf{\theta}^{i}\) represents the \(j\) policy network parameters of the agent. Firstly, the strategy network inputs \(s\) into the Transformer model. Next, learn the mapping relationship between state information and action probability distribution through three fully connected layers, and finally output the probability corresponding to each action in that state. The final fully connected (FC) layer specifically adopts the Softmax activation function to ensure that the output value probabilistically satisfies the definition of the probability density function. Similarly, the value network also adopts an almost identical structure, as shown in Figure 8. The goal of the value network is to fit the \(S\) state value of the current state \(V_{\pi}(s)\), so only one output value is needed \(v(s;\mathbf{\omega}^{j})\) (where \(\mathbf{\omega}^{j}\) represents the agent's \(j\) value network parameters). The structure of the target value network is completely consistent with the value network, and its parameters are represented as \(\hat{\mathbf{\omega}}^{j}\).
## 3 Control Strategy and Optimization Function
### 2 Dof-Pidf Controller
#### 4.1.2 Jellyfish Swarm  
When jellyfish are in a swarm, they can move either passively (Type \"A\") or actively (Type \"B\"). Initially, most jellyfish in the swarm exhibit Type \"A\" motion, but they gradually transition to displaying more Type \"B\" movements. During Type \"A\" motion, jellyfish move within their current location, and the position of each jellyfish is updated according to:  
\[X_{j}(t+1)=X_{j}(t)+\gamma\times(B_{u}-B_{l})\times rand(0,1) \tag{25}\]  
The motion coefficient, represented by \(\gamma\), is associated with the extent of movement around the jellyfish's position. The lower bound and the upper bound are denoted by \(B_{l}\) and \(B_{u}\), respectively. To simulate the second motion, the jellyfish of interest (\(j\)) is chosen at random, and a vector from the chosen jellyfish (\(r\)) to the jellyfish of interest (\(j\)) is utilized to calculate the direction of movement. The selected jellyfish (\(r\)) will move toward the jellyfish of interest (\(j\)) when the latter's location has more food than the former's does. If the amount of food accessible to the chosen jellyfish (\(r\)) is less than that available to the jellyfish of interest (\(j\)), it immediately moves away from it. Each jellyfish in a swarm navigates towards  
Figure 4: Jellyfish distribution in the ocean.  
the optimal direction in search of food. The direction of motion (Figure 5) is denoted by Equation (27), and the new position of the jellyfish is simulated by Equation (28).  
\[\overrightarrow{step}=X_{j}(t+1)-X_{j}(t) \tag{26}\]  
\[\overrightarrow{dir}=\left\{\begin{array}{c}X_{r}(t)-X_{j}(t)\\ X_{j}(t)-X_{r}(t)\end{array}\right\}\qquad if\qquad\left\{\begin{array}{c}O( X_{r})\leq O(X_{j})\\ O(X_{r})>O(X_{j})\end{array}\right\} \tag{27}\]  
where the objective function of position \(X\) is \"\(O(\cdot)\)\" and  
\[X_{j}(t+1)=\overrightarrow{step}+X_{j}(t) \tag{28}\]
## 4 Results and Discussion  
### Tunable Optical Spectra  
Figure 2 is the optical spectra of the ECDLs, which was measured by an optical spectrum analyzer (OSA, YOKOGAWA, AQ6370D, Tokyo, Japan) with a resolution of 0.02 nm. The injection currents are, respectively, 150 mA and 200 mA for Figure 2a,b. The tunable optical spectra were obtained by adjusting the grating along the vertical direction. As shown in Figure 2, the optical spectra of the ECDL show a wide tunable range and excellent SMSR. It can be seen that the tunable range remains unchanged when the injection current increases from 150 mA to 200 mA. The tunable range is 40 nm from 1525 nm to 1565 nm. During the tuning process, mod hopping may occur. In our experiment, the tunable range is limited by the bandpass of the filter when the gain is relatively high. For the interference filter, the bands outside the passband have very low transmittance. Additionally, the gain of the external cavity laser outside the passband decreases rapidly. As can be seen from the figure, the ECDL has high SMSR during the whole tuning range with an injection current of 200 mA.
# Voice Maps as a Tool for Understanding and Dealing with Variability in the Voice
## 2 Aspects of Variability in Voice
### Representativeness and Elicitation  
It is rarely a good idea to reduce any given aspect of a voice to a single number. Still, if we wish to do so, such as for making group comparisons, then it is necessary to minimize the noise from as many sources as possible, and to ensure that the number obtained really is representative of the given person's current vocal status. So, to what degree is an elicited vowel representative of the person's habitual voice? This question was addressed by [PERSON] et al. [40], who found that sustained vowels tended to be produced at a somewhat different \(f_{\mathrm{o}}\) than that of habitual speech, with age and sex effects as well (\(-\)2.6 to + 1.1 semitones difference). SPL was not reported in that study. The authors ventured that there may be a \"performance\" aspect to sustaining a vowel that might bias its production away from that in habitual speech. Others have found that test-retest reliability appears to be greater for a reading task than for elicited sustained vowels, as shown for \(f_{\mathrm{o}}\) by [PERSON] [42].  
A reading task is more like normal speech, and so we may expect it to be more ecologically valid than isolated sustained vowels. So, is it possible to measure things from running speech, even though its inherent variability is a concern? The speech range profile (SRP), of course, does this in what regards limits and ranges in \(f_{\mathrm{o}}\) and SPL. The long-time average spectrum (LTAS) can be used for assessing some voice properties, if the averaging is performed over a sufficiently long and homogenous task. Some studies indicate that the CPP is applicable to the assessment of voice problems using reading tasks, although problems remain with separating voiced from unvoiced segments [43]. For singing, it has been found [19] that EGG waveforms of trained female singers were distributed similarly over the entire range of a song, regardless of whether the singer's task was to sing on sustained /a:/ vowels, on/pa:/ syllables, or on lyrics; although the EGG waveforms were somewhat more variable for the latter, as would be expected.
## 3 Field Tests for Energy Slab  
### In-Situ Thermal Response Tests (TRTs)  
In order to evaluate the heat exchange performance of the energy slabs, in-situ TRTs were conducted. Figure 7a shows a schematic description of the TRTs. In the TRTs, temperatures of the inlet and outlet fluid were continuously monitored while applying constant heat injection. Then, the effective thermal conductivity of the ground could be estimated by analyzing the in-situ TRT data complying with the infinite line source model [19; 20; 21]. This value is closely related to the heat exchange performance of installed GHEXs. The effective thermal conductivity of the ground can be obtained from Equation (1):  
\[\Delta\mathrm{T}=\frac{Q_{inject}}{4\pi\lambda\mathrm{L}}\ln(\mathrm{t})+ \frac{Q_{inject}}{\mathrm{L}}[\frac{1}{4\pi\lambda}\ln(\frac{4\alpha}{\gamma {\mathrm{r_{b}}}^{2}})+{\mathrm{R_{b}}}] \tag{1}\]  
However, the infinite line source model works under several critical assumptions. First, the surrounding media are regarded as homogeneous. Thus, the effective thermal conductivity represents the equivalent thermal conductivity of overall media, such as ground formations, concrete and GHEXs. Second, the heat source is assumed as an infinite line-shaped heat source. However, the energy slabs exhibit a horizontally layered configuration, in which the heat source is far from thread-shaped. Therefore, a new terminology of relative heat exchange efficiency per unit pipe length (_eff\({}_{pipe}\)_) is introduced to indirectly compare the heat exchange performance of constructed energy slabs [2; 22].  
Figure 6: Views of constructed energy slab. (**a**) Side view of energy slab; (**b**) Front view of energy slab.  
Figure 7: Schematic of in-situ field tests (thermal response tests (TRT), thermal performance tests (TPT)).  
This term can be used to evaluate the heat exchange performance of various types of energy slabs. The relative heat exchange efficiency per unit pipe length is calculated by Equation (2):  
\[eff_{pipe}=\frac{Q_{c\_inject}}{slope\times L_{pipe}} \tag{2}\]  
where the _slope_ is the linearized relation between average temperature of inlet and outlet, and the natural logarithm of elapsed time.  
The relative heat exchange efficiency per unit pipe length of energy slabs was compared with consideration to the pipe materials. As the TRTs continuously inject high heating power, and the absence of resting period during the test, the influence of ambient air temperature is not significant. Consequently, the wall-type and floor-type energy slabs were compared for the relative heat exchange efficiency per unit pipe length of the energy slabs regardless of the existence of the thermal insulation layer. In a series of TRTs, the average flow rate of circulating fluid was maintained at 29.8 lpm, and the test duration was set to 2880 min for all the cases.
## 3 Development of the EMS and Obtained Results for a Specific Case Study
### Generalization and Adaptive Approach Applied to the FL EMS
#### 3.6.2 Adaptive Approach of the Optimized FL EMS by Electricity Price Forecast  
In order to adapt the MFs to the day-ahead price profile, an average price profile is calculated from the seasonal profiles (Figure 16), and a relationship is inferred between that profile and the optimized FL price MFs, as explained below. In Figure 16, the average price profile is shown with the optimized FL MF top values.  
A clear relation between the MF top values and the average price profile can be seen, and so the MF top values are defined as the next equations in the adaptative EMS:  
\[VL_{top}=p_{min}+0.05\cdot(p_{max}-p_{min}) \tag{17}\]  
\[VH_{top}=p_{min}+0.95\cdot(p_{max}-p_{min}) \tag{18}\]  
\[L_{top}=p_{min}+\frac{(p_{max}-p_{min})}{3} \tag{19}\]  
Figure 16: Annual average profile of the electricity price and FL EMS price MF top values.  
Figure 15: BESS output power and electricity power profiles for the month scenario.  
\[H_{top}=p_{min}+\frac{2\cdot(p_{max}-p_{min})}{3} \tag{20}\]  
The top values are almost proportionally distributed in the range from the minimum, \(p_{min}\), to the maximum, \(p_{max}\), price of the day. It should be noted that the limiting top values (VL and VH) are not on the maximum and minimum values of the price profile.  
Regarding the maximum and minimum values of the MFs, they are fixed to have the same value as the top value of their adjacent MFs; in the optimized FL EMS, it can be seen that the values do not differ much from this logic (Figure 14). Moreover, now that the price MF system has been fixed, the top value of the 'average' \(SOC\) linguistic variable is fixed to 0.5, so that the BESS maintains its \(SOC\) near this value. This was not viable on the optimization of the EMS6, because only one price MF system was used for the whole scenario.  
Using this price forecasting logic, two FL types have been designed--a Mamdani FL and a Sugeno FL--in order to analyze what could lead to better results.
###### Abstract  
Pipe integrity is a central concern regarding technical safety, availability, and environmental compliance of industrial plants and pipelines. A condition monitoring system that detects and localizes threats in pipes prior to occurrence of actual structural failure, e.g., leakages, especially needs to target transient events such as impacts on the pipe wall or pressure waves travelling through the medium. In the present work, it is shown that fiber-optic distributed acoustic sensing (DAS) in conjunction with a suitable application geometry of the optical fiber sensor allows to track propagating acoustic waves in the pipeline wall on a fast time-scale. Therefore, short impacts on the pipe may be localized with high fidelity. Moreover, different acoustic modes are identified, and their respective group velocities are in good agreement with theoretical predications. In another set of experiments modeling realistic damage scenarios, we demonstrate that pressure waves following explosions of different gas mixtures in pipes can be observed. Velocities are verified by local piezoelectric pressure transducers. Due to the fully distributed nature of the fiber-optic sensing system, it is possible to record accelerated motions in detail. Therefore, in addition to detection and localization of threatening events for infrastructure monitoring, DAS may provide a powerful tool to study the development of gas explosions in pipes, e.g., investigation of deflagration-to-detonation-transitions (DDT).  
distributed acoustic sensing (DAS); distributed vibrations sensing (DVS); fiber-optic sensing; condition monitoring; pipeline integrity; gas explosion +
Footnote †: journal: Sensors