esa-sceva/satcom-chunk-collection huggingface dataset:
# Toward Efficient Mobile Electric Vehicle Charging under Heterogeneous Battery Switching Technology
## 3 System Model
### Proposed Scheme  
The proposed efficient heterogeneous battery switching scheme aims to minimize the average time for each type of vehicle that has to wait for battery switching while traveling. To facilitate the issue of the proposed scheme through equation and formulation, we define some notations which are given below.  
* \(A_{t}\): The \(A_{t}\) denotes the average waiting time for battery switching at each BSS. It includes queuing time (\(Q_{t}\)) of vehicle for BS and the duration of the switching process.
* \(\forall B_{SS}\): The \(\forall B_{SSS}\) presents the set of BSS in the network.
* \(V_{Q}\): The \(V_{Q}\) denotes the queue of requested vehicles arrived at station.
* \(W_{t}\): \(W_{t}\) shows the overall waiting time of all vehicles in a BS. It is computed by including \(Q_{t}\), battery recharging time \((B_{rt})\) and switching time (\(S_{t}\)).  
With this notation, the problem is then formulated as follows:  
\[W_{t}=\sum_{I\in\mathbb{V}_{BSS}}V_{Q}\cdot A_{t} \tag{1}\]  
The \(W_{t}\) of \(V_{i}\in U_{1}\) can be minimized if the switching demand of \(R_{V}\) is balanced well across all BSSs in the network. However, the uniform balancing across all BSS is infeasible in complex and congested city scenarios because complete orchestration among all network entities is demanded. Hence, our approach is to achieve local optimization first and then consider global optimization.  
The time to switch a battery is the main source of waiting time. However, we assume that this attribute is fixed to 3 min as a constant value across all BSS in the network. However, as compared to switching time, queuing and waiting time takes a much longer time at BSS in process of BS. Therefore, our focus is to find an optimal BSS to minimize requested vehicle (\(R_{V}\)) queuing time, which will be discussed in the next section.  
Figure 2: Flowchart of proposed work: Global Controller (GC), Set of BSS (\(BSSi\)), Waiting Time (WT), Requested Battery (RB), Reservation Request (RQ), Estimate Charging Time (ECT) and Estimate Switching Time (EST).
# 108 m Underwater Wireless Optical Communication Using a 490 nm Blue VECSEL and an AOM
## 4 Results and Discussion
### Acousto-Optic Modulator Bandwidth Assessment  
The external modulation of laser beams uses the Bragg diffraction principle of acousto-optic crystals. As shown in Figure 5a, when the radio frequency (RF) signal is applied to the piezoelectric transducer, it is transformed into an ultrasonic signal and transmitted to the acousto-optic crystal. The ultrasonic wave induces periodic fluctuations in the refractive index of the acousto-optic crystal, resulting in the formation of a phase grating. When the incident light enters the acousto-optic crystal, the angle of incidence determines the diffracted light, and the acousto-optic crystal produces first-order diffraction, carrying the signal only if the Bragg diffraction angle is satisfied. Acousto-optic modulators can achieve diffraction efficiencies of up to 70%.  
Figure 4: (**a**) Schematic of the 108 m distance UWOC system based on blue VECSEL. AOM in the picture means acousto-optic modulator; AWG in the picture means arbitrary waveform generator; PPM in the picture means pulse position modulation. (**b**) Transmitter of the UWOC system. (**c**) Underwater reflector. (**d**) Receiving equipment is avalanche photodiode (APD). (**e**) Underwater communication link and mixed-signal oscilloscope (MSO).  
The ultrasonic carrier frequency of the AOM was 250 MHz. Considering the bandwidth limitation of the AOM, we measured the modulation of the optical signal by the AOM. As shown in Figure 5b, the signal modulation depth at 10 MHz bandwidth was normalized to measure the modulation depth at different signal pulse lengths. The signal modulation depth did not change much at a pulse width of 50 ns, corresponding to a time slot frequency of 20 MHz. However, at 100 MHz, the modulation depth was only 30% of that at 10 MHz. The corresponding eye diagrams at different frequencies are shown in Figure 6a-d.
## 4 Training Setup
### Simulation Framework  
The software architecture applied to train the different agents with dynamics randomization is extended from our previous work in [14] and shown in Figure 5.  
Figure 4: The side wheel forces \(F_{\mathrm{s}}^{\mathrm{W},}\) over the side slip angle \(\alpha_{i}\) for different values of \(\mu\) with \(i\in\{\ell,r\}\) denoting the front or rear wheels, respectively, according to [PERSON]’s MF [18] in Equation (A14).  
Figure 5: Training setup of the PFC task including dynamics randomization extended from [14].  
Here, the reinforcement learning environment for the PFC problem is implemented using the Python-based OpenAI Gym framework [20]. This framework offers a standardized interface with several reinforcement learning libraries, such as the Stable-Baselines 2 library [21], used in this work. The vehicle model is written in Python as a system of ordinary differential equations (ODEs) and solved by the odeint-function from the Scipy library [22]. Furthermore, time independent path interpolation (TIPI) [12; 23] is applied to determine the closest point on the reference path for each time step, which is then used by the agent to learn how to steer the vehicle towards the path. The implementation details of the TIPI are shown in Appendix A. The TIPI is implemented in Modelica [24] and exported by Dymola as a Functional Mock-up Unit (FMU) [25], which contains the compiled code of the TIPI algorithm. This FMU is then incorporated into the Python-based reinforcement learning framework.
## 1 Introduction
In another spectral domain, thermal remote sensing has been shown to detect small changes in stress levels caused by reduced transpiration under water-stress conditions, and it has also displayed potential for yield screening [22]. Therefore, high-resolution (_i.e._ below 50 cm pixels) thermal and hyperspectral imagery (mainly deployed on-board aircraft or unmanned vehicles) is capable of providing information over small plots typically used by crop breeders to calculate several vegetation and spectral indices at the canopy level. Such indices derived from both thermal and hyperspectral imagery are related to specific leaf biochemical and canopy biophysical parameters related to crop growth and function. The application of chlorophyll fluorescence in this context is still at a very early stage but recent results indicate that fluorescence quantification from hyperspectral imagers were able to detect stress levels [17, 23]. Some authors have suggested the application of one or more imaging technique in a multi-sensor approach where thermal sensing in combined with reflectance, fluorescence, and other sensing technique [24, 25]. The aim of this study was to determine whether the airborne hyperspectral and thermal-derived indicators acquired from an aircraft can be used to assess physiologically-based plot variability in the context of plant phenotyping, evaluating the effects on wheat yield under rainfed conditions in the Mediterranean area. Specifically, the study evaluated narrow-band hyperspectral indices and the quantification of chlorophyll fluorescence emission as indicators of yield variations under water-stress conditions.
## 2 Materials and Methods
### Analysing Tissue-Specific Protein Expression of Lycopene Targets in Humans  
All the identified targets of lycopene with affinity values below 1 uM were evaluated for their tissue-specific protein expression using the Human Protein Atlas ([[https://www.proteinatlas.org](https://www.proteinatlas.org)]([https://www.proteinatlas.org](https://www.proteinatlas.org))) database [37; 41]. Of the six high-affinity targets identified, we could analyse only five targets, as the protein expression data of _SLC6A5_ was not available. The tissue-specific protein expression data of the five high affinity lycopene targets (_MAP2K2_, _SCN2A_, _SCN3A_, _TOP2A_, and _TRIM24_) were plotted using a Venn diagram to assess the logical relationships between the expression pattern of the targets and to identify if any specific tissue type is of preference for lycopene pharmacological effects.
## 5 Sleep Stages Classification Using DL Models  
### Different Stages of Sleep  
According to [PERSON] and [PERSON] (R and K) [51], humans can experience six discrete stages during sleep: (1) wakefulness (W), (2) rapid eye movement (REM) sleep, and (3) four stages of non-REM (NREM) sleep (S1 to S4) [52]. Based on the sleep electroencephalogram (EEG) characteristics, W occurs when the brain is most active, which is represented by high frequency of alpha rhythms. In the NREM sleep, these alpha rhythms eventually diminish when entering the S1 wherein theta rhythm dominates instead. In the S2, sleep spindles and occasional K-complex waveform will appear. The K-complex waveform usually lasts for approximately 1 to 2 s. The S3 sleep occurs when low frequency delta rhythms appear intermittently and eventually, they dominate in the S4 sleep. Finally, REM sleep usually follows after the S4 sleep. In the REM sleep, theta rhythms resurface again, but unlike in the S1 sleep, theta rhythms are accompanied with EEG flattening [52]. Following the guidelines from American Academy of Sleep Medicine (AASM), the S3 and S4 sleep stages can be merged into one sleep stage S3, because of the similarity in their characteristics [21]. Since the delta rhythms are the slowest EEG waves, S3 and S4 sleep stages are known as Slow Wave Sleep (SWS) or the deep sleep. Thus, most sleep classification studies are based on five: W, S1, S2, S3, and REM sleep stages, instead of six (Figure 5).  
Figure 4: Basic autoencoder architecture.
## 3 Magnetic Ferrite Nanoparticles for Wastewater Treatment
### Heavy Metals  
Heavy metals, such as lead (Pb), mercury (Hg), cadmium (Cd), arsenic (As), and chromium (Cr), are naturally occurring elements. However, anthropogenic activities such as mining, industrial discharges, agricultural runoff, and improper waste disposal have significantly increased their concentrations in aquatic environments [75]. Unlike organic pollutants, heavy metals do not degrade over time; they persist in the environment, leading to bioaccumulation in aquatic organisms and biomagnification through the food chain [76; 77; 78]. As a result, exposure to heavy metals can cause acute and chronic toxicity in both wildlife and humans [79; 80]. The persistence of heavy metals in water is exacerbated by their ability to bind to sediments, making conventional water treatment methods less effective [81; 82]. Heavy metals can also interact with other pollutants, altering their chemical forms and toxicity.  
In many cases, heavy metals exist as part of complex mixtures, making it difficult to assess their individual and collective impacts [83; 84; 85]. Furthermore, factors such as pH and temperature influence the mobility and bioavailability of these metals, complicating the prediction and management of their environmental effects [86; 87; 88]. The common practice of heavy metal removal is adsorption, and high surface area magnetic nanoparticles, for this purpose magnetic ferrite nanoparticles are ideal candidate [25]. Many reports exist on ferrite nanoparticle application in different heavy metals' removal [89; 90].
# End-to-End Multi-Modal Speaker Change Detection with Pre-Trained Models
## 4 Experiments
### Evaluation Metrics  
Several metrics are employed to assess the performance of the baseline and proposed methods. The false alarm rate (FAR) measures the proportion of incorrect speaker change detections, which is calculated as the ratio of false positives to all non-change points:  
\[\text{FAR}=\frac{\text{False Positives}}{\text{False Positives}+\text{True Negatives}} \tag{7}\]  
The missed detection rate (MDR) evaluates the frequency of undetected speaker changes, which is determined as the ratio of false negatives to all true change points:  
\[\text{MDR}=\frac{\text{False Negatives}}{\text{True Positives}+\text{False Negatives}} \tag{8}\]  
The Hit rate measures how well the model correctly identifies actual speaker change points.  
\[\text{Hit}=\frac{\text{True Positive}}{\text{Total Actual Positives}} \tag{9}\]  
Additionally, precision is used to indicate the accuracy of detected speaker changes, recall assesses the ability to identify all true speaker changes, and the F1-score provides a balanced measure of precision and recall. These metrics collectively offer a comprehensive evaluation of the model's effectiveness in speaker change detection. It should be noted that the average results (Precision, Recall, F1-score, FAR, and MDR) reported below are calculated using the Macro method.
# Ocean Surface Topography Altimetry by Large Baseline Cross-Interferometry from Satellite Formation
## 4 CFS for Baseline Decorrelation Compensation
### Design Principle of CFS
#### 4.2.1 Radar Look Angle  
During the selection of CFS, the swath width of LB-IRA and SNR should be taken into consideration while choosing the proper radar look angle. The look angle varies within a certain range to acquire a swath width of 50 km, at least. According to Figure 8, a smaller center look angle leads to wider variation range of look angle in order to cover up the swath width, and eventually, larger CFS variation range since the frequency shift is determined by radar look angle.  
However, the CFS method can only select a constant frequency shift for the entire swath. The selected CFS may perfectly compensate for the baseline decorrelation at the swath center, however, it leaves the other part of the swath only partially compensated. Figure 10 shows the spectrum overlap before and after CFS; around the center range where CFS is selected accordingly, the spectrum perfectly overlaps with each other after CFS. However, due to the variation of radar look angle, the spectrum only partially overlaps with each other around the near or far range of the swath.  
Figure 11a,b shows the coherence before and after CFS compensation, the cross-track baseline length and bandwidth are set to be 1000 m and 40 MHz, respectively. The look angle ranges of 9-12\({}^{\circ}\) and 12-15\({}^{\circ}\) cover up nearly the same swath width of 50 km, however the CFS variation of 9-12\({}^{\circ}\) is larger than that of 12-15\({}^{\circ}\). Before CFS compensation, as shown in Figure 11a, the coherence is zero from the swath center to the near swath since the spectrum has no overlap part due to a smaller look angle. Only a small part of the spectrum overlaps with each other near the far range as the radar look angle increases, due to limited signal bandwidth. After CFS compensation, as shown in Figure 11b, although both of the coherence are high at the swath center (corresponds to center look angle), the coherence of 9-12\({}^{\circ}\) decreases more sharply than 12-15\({}^{\circ}\) due to larger CFS variation, as indicated by the dashed line in Figure 11a. Apparently, 12-15\({}^{\circ}\) is more preferable since the coherence is higher and more consistent among the entire swath.  
However, too-large look angle may decrease the SNR significantly since the normalized radar cross-section (NRCS) decreases as radar look angle increases, and the decorrelation by thermal noise will get worse. Referring to the 1\(\sigma\) NRCS from Global Precipitation Mission (GPM) [31], 12-15\({}^{\circ}\) of radar look angle is selected for LB-IRA, which compensates for the baseline decorrelation well among the entire swath and maintains a reasonable SNR level as well.  
Figure 10: Spectrum overlap before and after CFS along the range direction.
# An Advanced Crow Search Algorithm for Solving Global Optimization Problem
## 4 Numerical Examples
### Engineering Problems
#### 4.2.4 Weight of a Three-Bar Truss Problem  
This problem aims to find the minimum truss weight that satisfies the constraints when a load (\(P\)) is applied to a truss structure of three members, such as in Figure 12. The design variables are \(A_{1}\) (cross-sectional area of Member 1; \(x_{1}=x_{3}\)) and \(A_{2}\) (cross-sectional area of Member 2; \(x_{2}\)), and the range that the design variables can have is given by Equation (23). The three-bar truss weight minimization problem is expressed as an equation by Equation (24). Here, the distance (\(L\)) of the node is 100 cm, the load (\(P\)) is 2 \(\mathrm{kN/cm^{2}}\), and the maximum stress (\(\sigma_{max}\)) is 2 \(\mathrm{kN/cm^{2}}\). In addition, each design variable has the constraints provided in Equation (25). The maximum number of generations (\(t_{max}\)) was set at 20 in this problem.  
\[0.0\leq x_{1}\;\text{or}\;x_{2}\leq 1.0 \tag{23}\]  
\[min\;f(x)=(2\sqrt{2x_{1}}+x_{2})l \tag{24}\]  
\[\begin{split} Subject\;to\;:& g_{1}(x)=\frac{ \sqrt{2}x_{1}+x_{2}}{\sqrt{2}x_{1}^{2}+2x_{1}x_{2}}P-\sigma\leq 0\\ g_{2}(x)=\frac{x_{2}}{\sqrt{2}x_{1}^{2}+2x_{1}x_{2}}P-\sigma \leq 0\\ g_{3}(x)=\frac{1}{\sqrt{2}x_{2}+x_{1}}P-\sigma\leq 0\end{split} \tag{25}\]  
Table 11 shows the results of the ACS algorithm and those of a previous study [14]. Here, SoC, MB, and DSS-MDE refer to the society and civilization (SoC) algorithm, the mine blast (MB) algorithm, and the dynamic stochastic selection with multimember differential evolution (DSS-MDE) algorithm. The ACS algorithm determined the weight of the three-bar truss structure to be 263.895843 (the design variables were 0.7887 and 0.4081), and all of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Design Variables** & **[PERSON] [24]** & **[PERSON] [31]** & **[PERSON] [32]** & \multicolumn{3}{c}{**This Paper**} \\ \cline{4-6}  & & & & **Conventional CS** & **ACS** \\ \hline \(x_{1}\) & 0.0515 & 0.0534 & 0.0500 & 0.0520 & 0.0517 \\ \(x_{2}\) & 0.3517 & 0.3992 & 0.3159 & 0.3642 & 0.3578 \\ \(x_{3}\) & 11.6322 & 9.1854 & 14.2500 & 10.8626 & 11.2240 \\ \(g_{1}(x)\) & \(-\)0.00218 & 0.00002 & \(-\)0.00001 & \(-\)4.5997 \(\times 10^{-5}\) & \(-\)20183 \(\times 10^{-11}\) \\ \(g_{2}(x)\) & \(-\)0.00011 & \(-\)0.00002 & \(-\)0.00378 & \(-\)7.9229 \(\times 10^{-5}\) & \(-\)2.6946 \(\times 10^{-10}\) \\ \(g_{3}(x)\) & \(-\)4.02632 & \(-\)4.12383 & \(-\)3.9380 & \(-\)4.0677 & \(-\)4.0560 \\ \(g_{4}(x)\) & \(-\)4.02632 & \(-\)0.69828 & \(-\)0.75607 & \(-\)0.7225 & \(-\)0.7270 \\ \hline \(F(x)\) & \(1.2704\times 10^{-2}\) & \(1.2730\times 10^{-2}\) & \(1.2833\times 10^{-2}\) & \(1.2669\times 10^{-2}\) & \(1.2665\times 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Results of weight of a spring problem.  
Figure 12: Weight of a three-bar truss problem.  
the constraints were satisfied. The result of the ACS algorithm was lighter than the results of the conventional CS algorithm and Askarzadeh.
## 3 Underwater Acoustic Sensor Network Models and Query Models
### Query Models  
Users frequently request on-demand data transmissions using queries. Most of the queries are condition-based selection queries (requests for data transmissions under certain conditions), and projection queries (requests for transmissions of certain attribute values of sensor data). Users can also demand periodic data transmissions and stop the responses after a certain period. Based on these observations, all queries can be represented by the following model:  
\[Q=o\begin{bmatrix}T\\ (S,D)\end{bmatrix}\]  
where:  
O = query operation, a one-time data request (a finite combination of selection and projection queries: \(o\in Span<\sigma\)).  
T = data reply period in terms of unit time  
S = query reply start time represented by the number of time units after the nodes query set start time (for a detailed explanation of query set start time, refer to Section 5.3.1.)  
D = query deadline also represented by the number of time units after the query set start time  
The queries' temporal information (period, start time and deadline) is arranged in terms of a unified atomic time unit. The specific length of the time unit is determined prior to all query operations. The following are two examples of our query model.  
Example (1)  
\(Q_{!}\): Transmit the pressure value for sensor S5 every 2 s for 30 s (unit time: 1 s).  
\[Q_{!}=\Pi_{\mathit{pressure}}\left(\sigma_{ID=S_{5}}\left(S\right)\right) \begin{bmatrix}2\\ (0,30)\end{bmatrix}\]  
Variable S refers to a dummy relation variable for a sensor node relation.  
Example (2)  
\(Q_{!}\): Transmit the pressure and temperature values every 3 s for 15 s if the temperature is above 20 degrees and the depth is greater than 50 meters (unit time: 1 s).  
Figure 2: Example of a relational underwater sensor network database model. The sensor network in **(a)** can be mapped to a relational database model in **(b)** where each of the sensors is mapped to one of the current relations.  
\[Q_{2}=\Pi_{\mathit{presure},\mathit{temp}}\left(\sigma_{\mathit{temp}>20\, \wedge\,z-\mathit{loc}>50}\left(S\right)\right)\bigg{|}_{\left(0,15\right)}^{3}\]  
When we refer to a \"query\", by definition it refers to the periodic data transmission request over a certain period of time. We hereby note, however, that in the following sections, the notation \"query\" may also frequently refer to a one-time data request in the continuous transmission task at some moment (or simply, a query operation), as long as no specific temporal information is mentioned and there is no ground for confusion. For example, \"responses to queries \(q_{1}\) and \(q_{4}\)\" would refer to the relations resulting after the two query operations are executed at a given time frame.
## 4 Discussion
### Framework Agreements and GPA  
Framework agreements have generally \"involved a contracting authority (or authorities) advertising an opportunity and then entering into a contract or other arrangement with one or more economic operators for the provision of works, supplies or services over a fixed period\" [27] (p. 2). The rationale behind framework agreements is to save costs and time for procurement procedures. Generally speaking, the duration of an agreement is no more than four years, during which the contracting bodies can award one or more operators under the framework with no further tendering process or a mini-competition process [28]. Our finding suggests that when comparing the contracts within framework agreements, those without framework agreements have a higher possibility of being green, by 3.1%. Framework agreements tend to hinder the application of green award criteria in award notices, which is supported by results in three subsamples of products, services, and works. This method of purchasing increases the efficiency of projects by limiting the number of tenderers. However, public authorities may miss also the economic operators who possess green technologies and managements.  
From our results, we determine that GPA is positively related with green procurement, and this effect is more obvious in products purchasing. GPA encourages public authorities to address environmental considerations in technical specifications and award criteria. Article X-6 provides technical specifications to\" promote the conservation of natural resources or protect the environment\" and evaluation criteria includes environmental characteristics from Article X-9. What is more, GPA includes the commitment to build a Work Program to \"promote the use of sustainable procurement practices, consistent with the Agreement\" [17]. These avocations in GPA come into effect from our analysis.