esa-sceva/satcom-chunk-collection huggingface dataset:
# Far Off-Resonance Laser Frequency Stabilization Technology
###### Abstract  
In atomic physics experiments, a frequency-stabilized or 'locked' laser source is commonly required. Many established techniques are available for locking close to an atomic resonance. However, in many instances, such as atomic magnetometer and magic wavelength optical lattices in ultra-cold atoms, it is desirable to lock the frequency of the laser far away from the resonance. This review presents several far off-resonance laser frequency stabilization methods, by which the frequency of the probe beam can be locked on the detuning as far as several tens of gigahertz (GHz) away from atomic resonance line, and discusses existing challenges and possible future directions in this field.  
laser; far off-resonance; frequency stabilization +
Footnote †: journal: Applied Sciences
# Multipath Mitigation for BOC Signals Based on Prompt-Assisted-Offset Correlator
## 4 Performance Evaluation and Results
### Simulation Results
#### 4.3.2 Static Multipath Scenarios
This is because the PAOC technique has thermal noise performance loss compared with using the prompt correlator. When the multipath relative phase is 0 and \(\pi\), the carrier multipath error is almost 0, and the carrier RMSE contains only the thermal noise error. Since the PAOC-PAOC method greatly improves multipath performance, the thermal noise performance loss is acceptable. Figure 11: RMSE envelop of various methods for the BOC(15, 2.5) signal, where the multipath relative phase is \(\pm\pi/2\): (**a**) subcarrier RMSE; (**b**) carrier RMSE. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Subcarrier** & **Subcarrier** & **Carrier** & **Carrier** \\  & **(\(\theta_{m}=0\&\pi\pi\))** & **(\(\theta_{m}=\pm\pi/2\))** & **(\(\theta_{m}=0\&\pi\pi\))** & **(\(\theta_{m}=\pm\pi/2\))** \\ \hline DBT & 0.51 & 0.09 & 1.96 & 8.84 \\ OC-P & 0.28 & 0.08 & 1.98 & 8.72 \\ OC-OC & 0.28 & 0.07 & 3.77 & 6.47 \\ PAOC-PAOC & 0.15 & 0.05 & 4.04 & 4.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Area enclosed by RMSE envelopes of the four methods for the BOC(15, 2.5) signal.
# Hierarchical Task Assignment and Path Finding with Limited Communication for Robot Swarms
## 4 Experimental Evaluation
### Experimental Setup  
To model our service robot domain, tasks are generated within two macro-areas at a time, chosen from 16 areas that cut the environment in identically-sized regions. Chosen areas generate tasks at a fixed task generation rate, set to 1 task per time-step in all environments, unless the area is completely full. Additionally, the enabled macro-areas change during the simulation eight times, resulting in a dynamic demand across the environment. Tasks do not disappear after a region change and can still be served by the robots in the area. We believe this to be an effective and realistic approximation of the service robot problem, in which a region--once served--does not require further attention for a certain amount of time.  
Unless otherwise specified, the settings for the experiments are as follows. The work time \(t_{i}^{\text{in}}\) for all tasks is constant and fixed to 5 steps, i.e., the number of steps a robot has to remain over a task to consider it accomplished. The number of robots involved in the simulations is related to the number of free-cells in the considered environment: 25 for split16 and facility, 50 for empty32, and 150 for lab. We consider nodes associated to areas of size 2 (i.e., \(a_{i}^{\text{in}}=(x_{i},y_{i},2)\), with \(m=\log_{2}N-1\)) as leaves. The number of decisions per time-step (i.e., movements in the hierarchical tree) is set to \(D=M=\log_{2}(N)-1\), that is, the depth of the quad-tree, and the probability to switch from the ascending to the descending state, and vice versa, are set to \(P_{a}=P_{d}=0.5\). Each simulation was executed for 300 time-steps. All the parameter values have been determined on the basis of previous work or empirical evaluations and are not specifically tuned based on the environment to be tested (for a discussion about this choice, see Section 5).
## 1 Introduction  
### Problem Statement  
The 21 st century is marked by pressing topics such as smart cities, wastewater management, renewable energy, artificial intelligence (AI), climate change, and sustainable development. These issues are driving technological innovation and shaping the future of urbanization, environmental sustainability, and resource management [1].  
To contribute to sustainable development by increasing recycling, all European Union (EU) countries had to transpose the EU regulations concerning the separate collection of waste (paper, metal, glass, paper, and plastic) by municipalities, including households, until July 2020 [2].  
In the case of residential households, four different bins for each waste category must be installed, most likely next to each other, with different colors to be easily spotted by family members. Furthermore, the individual has to pay attention to choosing the right bin that corresponds to the waste category each time a specific waste is thrown. Sometimes, people could make involuntary mistakes by discarding the waste in the wrong bin, children up to a certain age find it difficult to choose the proper bin, children voluntarily dispose of waste in different bins just for fun, etc. Furthermore, the content of the four bins from inside the house must undergo an additional check when emptying into the corresponding four bigger bins from outside the house. This process is time consuming and is mandatory to prevent local authorities taxation for dumping waste in the wrong bin. Another issue is that several proposed bin setups from the specialized literature use algorithms that do not correctly identify the waste category.  
The authors designed and implemented an AI-based separate waste collection bin for residential use with four bins for each waste category in a single unit to solve these issues. Moreover, the proposed prototype bin is used to validate two algorithms in correctly identifying the type of waste category. One proposed algorithm integrates a solution developed by Microsoft Azure Custom Vision Service 2024 (ACVS). This solution performs training on a large volume of data, with each volume corresponding to a specific class of waste. The second algorithm employs a Google Vision API Service 2024 (GVAS) solution, which generates a description of the identified object. This description provided by GVAS is further used to build a dataset. The new dataset ultimately serves to train a Custom Waste Sorting Model (CWSM) for the residential bin. The computed metrics highlight the advantage of the CWSM that can be integrated as real-time solutions that take the object and place it into the correct bin in a shorter time.
## 5 Discussion
Our proposal leverages the simplicity, relatively low cost, and versatility of a single intra-cavity filter with comb-like spectral features for tunable and multi-wavelength emission. This approach offers significant advantages over complex, hard-to-manufacture systems. For example, a two-mode fiber section spliced between SMF segments requires long nonlinear fiber (120 m) or SMF (3 km) to generate up to 10 lasing wavelengths with relatively high power fluctuations [28]. Other methods, like triple-core photonic crystal fiber or cascaded LPFGs, achieve fewer switchable wavelengths [29] and limited tunability due to the spectral finesse [30]. Twin photonic crystal fiber filters can achieve SMSRs above 57 dB for single-wavelength and 40 dB for multi-wavelength emission, though stability fluctuations reach 8.09 dB [31]. The SMF-SCF-SMF structure with enlarged splices allows tunable and multi-wavelength features via temperature, curvature, and polarization perturbations [32]. However, cross-sensitivities may affect output performance. Asymmetric LPFG demonstrates switchable multi-wavelength performance up to six wavelengths sequentially, comparable to the performance achieved in our research, but with larger output power variations of up to 6 dB [33]. The MMF-PMF-MMF filter induces the PHB effect for tunable single to sextuple wavelengths, requiring modification of the cavity by adding a Sagnac filter, complicating the design [34]. Our results demonstrate comparable or superior performance to previous studies, achieving superior stability with less complicated and relatively low-cost filters. Enhanced wavelength performance, characterized by precise tuning and minimal drift, makes this design an efficient and reliable solution for various practical applications. Compared to more complex designs, our approach offers significant advantages in terms of stability, performance, and cost-effectiveness. Finally, a comparative analysis of the tunable, switchable multi-wavelength fiber laser versus those utilizing concatenated tapered fibers and other structures is presented and summarized in Table 1. The study covers key aspects such as fiber filter structures, operational wavelengths, performance metrics, side-mode suppression ratios, and stability, highlighting significant differences and advantages. This examination emphasizes the impact of these factors on optimizing laser functionality and performance. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
## Materials and Methods  
Sodium hydroxide (NaOH, anhydrous pellets, \(\geq\)98%), phosphoric acid (\(\geq\)85%), absolute ethanol (EtOH abs. \(\geq\)98%, denaturated with 0.5-1.5% butanone), acetone (\(\geq\)99.5%), sodium bromide (\(\geq\)99%), sodium hypochlorite solution (NaClO, available chlorine 10-15%), sodium chlorite (NaClO\({}_{2}\), p.a.), 2,2,6,6-tetramethylpiperidin-1-oxyl (TEMPO, \(\geq\)98%), sodium hydrogen carbonate (\(\geq\)99.5%), quinine hemihydrate (\(\geq\)98%), glycerol (\(\geq\)99.5%), 2,2'-(ethylenedioxy)-bis-(ethylamine) (EDEA, \(\geq\)98.5%), \(N\)-(3-dimethylaminopropyl)-\(N^{\prime}\)-ethylcarbodiimide hydrochloride (EDC) and \(N\)-hydroxysuccinimide (NHS) were purchased from Sigma Aldrich (Vienna, Austria). Hydrochloric acid (HCl, 37%) and citric acid (\(\geq\)99.5%) were obtained from VWR International, LLC (Vienna, Austria) while glacial acetic acid (\(\geq\)99.8%) and urea (\(\geq\)99.5%) were purchased from Merck GmbH (Vienna, Austria) and Karl Roth GmbH + Co. KG (Vienna, Austria), respectively. Acetate buffer was prepared by adjusting the pH of 0.1 M acetic acid with 0.1 M NaOH to pH 4.8. Phosphate buffer was prepared by adding 0.1 M NaOH to 0.1 M phosphoric acid until a pH of 6.8 was reached. De-ionized water (DI H\({}_{2}\)O) was used if not stated otherwise.  
Bacterial cellulose (BC) was provided by the Research Centre for Medical Technology and Biotechnology (Forschungszentrum fur Medizintechnik und Biotechnologie, FZMB, Bad Langensalza, Germany) as differently sized sheets suspended in ethanol. The material was produced by a static cultivation of _Gluconacetobacter xylinum_ AX5 wild type strain on Hestrin-Schramm growth medium for 30 days at 30 \({}^{\circ}\)C. Following repeated alkaline treatment, the BC sheets were exhaustively washed with water and immersed in ethanol.
# Real-Time Prediction of Transarterial Drug Delivery Based on a Deep Convolutional Neural Network
## 2 Methods
### Data Preparation
#### 2.1.3 Injection Position and Geometry Representation  
We investigated SDF and binary image methods to represent the injection position and the blood vessel geometry. With SDF, the geometry of the blood vessels and the injection location were represented as a level-set function in the study area [38,39]. Due to the simple geometry of the straight blood vessel, the drug concentration field could be accurately predicted using only a binary image of the injection location and geometry representation, which is transformed into a matrix of 120 \(\times\) 120, shown in Figure 3a. We use a horizontal line through the injection site to indicate the injection location. The corresponding injected artery is presented in Figure 3b. The distance between the injection site and the lower artery wall is 5.5 cm. The inputs of the bifurcated blood vessels are the presented injection position and SDF geometry, as shown in Figure 3c,d. Unlike the case of the straight vessel, the value of the computational domain is set to represent the injection location, which is equal to the distance between the lower wall and the injection site. For the human hepatic artery system, in addition to the input strategy of the bifurcated blood vessel, we also explored another approach where SDF was used to represent both the injection location and the artery system geometry, as shown in Figure 3e,f. The former displays the SDF presented injection location, where the points on the horizontal line through the injection site are set to zero, then the values of other spatial points are set to be the nearest distance to the horizontal line. Figure 3f illustrates the SDF presented hepatic artery system, which we set to zero at the artery wall and the outlets. The SDF value of the region outside of the vascular geometry is masked, which allows the network to further focus on the drug distribution field within the blood vessels. The latter method achieves better prediction accuracy; for more detail, please refer to Section 3.2.1. For easy representation, the geometric horizontal and vertical coordinates of the three blood vessel types are all scaled to [0,1].
## 2 Affecting Factors
### Seasonal Changes
#### 2.2.3. Ecosystem Level  
MODIS and CHRIS/PROBA are common operational instruments for studying PRI at ecosystemic and regional scales [9; 60; 131; 132]. Alterations in the angle at which satellites detect seasonal fluctuations of illumination, the shadow fraction detected by the sensor and atmospheric effects were identified as critical influences on PRI signals on a spatial scale [31; 79; 133; 71], with the exception of some relatively uniform and dense canopy structures and a low variability of shadow fractions [71]. PRI obtained from the backscatter direction [25; 60; 132] nonetheless minimized the effect of shadows, and near-nadir satellite observations [79] that reduced the effects of soil background and atmospheric scattering were also optimal and improved the accuracy of RUE detection. PRI performance was improved in forward-scattering directions after replacing the PRI formulation wavelength, which indicates a possible shift in the signal of xanthophyllic de-epoxidation with the direction of detection. In contrast, [PERSON] et al. [134] found no difference between backward and forward effects on MODIS PRI in a dense evergreen and two deciduous forest ecosystems. MODIS PRI has detected the impacts of water stress on RUE [25], but not for severe droughts [31; 135]. Intra-annual changes in MODIS PRI were dependent on the composition of foliar pigments at evergreen sites and on the dynamics of canopy structure at deciduous sites [79], but a universally applicable model for correlatingecosystemic RUE with MODIS PRI for all types of ecosystems could not be found. Vegetation indices designed to be more sensitive to chlorophyllic content explained most of the variability in GPP in a subalpine grassland ecosystem characterized by a strong seasonally dynamic GPP, and the accuracy only slightly improved by adding PRI to the model formulation [136]. The combined use of PRI and VPD was also one of the better models for estimating RUE in tropical evergreen rainforests [77].
# Retransmission Avoidance for Reliable Data Delivery in Underwater WSNs
## 3 FLMPC: Proposed Protocols
### Neighbor Table  
Sensor nodes reside in transmission range and lying at the low depth than the current forwarder are its neighbors. In order to save energy because of excessive exchange of control messages among the network nodes, all the sensor nodes in proposed schemes maintain neighbor table. Initially, the sensor nodes broadcast control message in the network. This control message consists of source ID,  
Figure 4: Forwarder Node Selection in FLMPC-One. (**a**) Cross Node potential forwarder node (PFN); (**b**) Relay Node PFN.  
Figure 5: Layer Division.  
location (\(x\),\(y\)) and depth of source node. Receivers calculate their distance with source based upon receiving a control message by Euclidean distance formula which is given by:  
\[Dist_{(i,j)}=\sqrt{(x_{i}-x_{j})^{2}+(y_{i}-y_{j})^{2}} \tag{14}\]  
where \((x_{i},y_{i})\) and \((x_{j},y_{j})\) are the coordinates of source and receiver, respectively.  
Every sensor node maintains tuple which consists of (Source ID, Location, Distance, Depth) for each neighbor. The Location is (\(x\),\(y\)) coordinates of the sender retrieved from the control message, whereas, Distance is calculated using Equation (14), Depth shows that how far the node is from the surface of the water. When this information is acquired successfully, the sensor node maintains it's tuple of neighbor nodes.  
\[Dist_{(i,j)}\leq R_{t} \tag{15}\]  
\[Depth_{i}>Depth_{j} \tag{16}\]  
where \(R_{t}\) is the transmission range and Equation (15) ensures that the calculated distance must be less than or equal to the transmission radius. Receiver nodes also retrieve depth information from the control message to compare it with its own depth as shown in Equation (16). If Equation (16) evaluates to true, the receiver maintains tuple for the source node in its neighbor table. Depth of neighbors helps the sensor nodes to select forwarder which is at minimum depth among all selected neighbors while transmitting data packet. Both schemes do not maintain information of high depth nodes because they have no mechanism of backward transmissions and also to reduce the message overhead.
## 3 Results and Discussion
### Biosynthesis of AgNPs Using L. plantarum TA4 Biomass
When the cells are exposed to Ag\({}^{+}\), the silver cation will bind to the anionic surface, providing sites for the biosorption [11]. At this point, Ag\({}^{+}\) interacts with proteins and other functional groups of the cell that serve as an electron donor by reducing Ag\({}^{+}\) to its respective metal atom (Ag\({}^{0}\)) and subsequently developing AgNPs. Moreover, proteins may also bind to AgNPs through free amino or carboxyl groups, which serve as capping agents [43]. Therefore, it can be postulated that biomolecules and functional groups present in bacterial cells or secreted by the bacteria serve as the reducing agents and promote the production of AgNPs. To sum up, the cell biomass of _L. plantarum_ TA4 has demonstrated the potential as a nanofactory for the development of AgNPs without the use of harsh chemicals and high energy consumption. Subsequently, the formation of AgNPs was validated by FTIR analysis to determine the functional groups involved in the synthesis process.
# Compressive Sampling with Multiple Bit Spread Spectrum-Based Data Hiding
###### Abstract  
We propose a novel data hiding method in an audio host with a compressive sampling technique. An over-complete dictionary represents a group of watermarks. Each row of the dictionary is a Hadamard sequence representing multiple bits of the watermark. Then, the singular values of the segment-based host audio in a diagonal matrix are multiplied by the over-complete dictionary, producing a lower size matrix. At the same time, we embed the watermark into the compressed audio. In the detector, we detect the watermark and reconstruct the audio. This proposed method offers not only hiding the information, but also compressing the audio host. The application of the proposed method is broadcast monitoring and biomedical signal recording. We can mark and secure the signal content by hiding the watermark inside the signal while we compress the signal for memory efficiency. We evaluate the performance in terms of payload, compression ratio, audio quality, and watermark quality. The proposed method can hide the data imperceptibly, in the range of 729-5292 bps, with a compression ratio 1.47-4.84, and a perfectly detected watermark.  
compressive sampling; compressed sensing; watermark; data hiding; spread spectrum; singular value decomposition; Hadamard +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Pints in Inlet Channel
#### 3.3.3 Different Height  
The size structure of the inlet channel is influenced by the height of the pins, subsequently impacting both pressure drop and permeation velocity. In this model, the heights of symmetrical and staggered disturbance bodies are adjusted to within a range of 0.1-0.4 mm.  
With the increase in pin height, the permeation velocity at the front of the porous medium increases, while the permeation velocity at the back of the porous medium decreases. The change in symmetrical arrangement becomes more pronounced. By comparing the velocity distributions of both arrangements, it is evident that an increase in pin height results in a contraction of flow space at corresponding positions, leading to local flow velocity increases. This causes a gradual shift in fluid flow from the center to the side without pins and the space on both sides of the pins (particularly in symmetrical arrangements) [49], as shown in Figure 10a,b. When the pin height is 0.4 mm, the velocities on both sides tend to align with those at the center. When the pin height reaches 0.4 mm, the channel size at the pins is reduced to 0.3 mm, severely restricting gas flow. This significantly increases the local velocity and high pressure in the front of the inlet channel, thereby impacting the permeation rate. The comparison of permeation velocities at different pin heights in Figure 10c,d reveals that as the pin height increases, the permeation velocity at the same axial position increases, leading to enhanced pressure and permeation speed at corresponding positions.  
Figure 10: **(a,b)** Contours of axial inlet channel velocity, (**c,d**) curves of permeation velocity distribution, and (**e**) variance corresponding to different heights of pins arranged alternately and symmetrically.  
Figure 10e illustrates the variance in axial permeation velocity for both arrangements. As the pin height increases, the variance gradually decreases for both arrangements, indicating that within a height of 0.4 mm in our model, there is improved axial permeation velocity performance with increasing height, and the effect of the symmetrical arrangement is more obvious.
# How Will Rwandan Land Use/Land Cover Change under High Population Pressure and Changing Climate?
## 3 Results
### Driving Factors and Changes in Land Use/Land Cover
From Phase B to Phase C, there was a change in the main factors driving changes in unused land. The main factor limiting land use change during Phases A and B was precipitation, which had contribution rates of 50.22% and 55.75%, respectively. During Phase C, soil and topography became the main factors limiting the change in unused land. The contribution rates of soil to the change in unused land were 6.35%, 13.23%, and 35.65%, respectively, during Phases A, B, and C, showing a gradually increasing trend. Figure 6: Contribution rates of driving factors of different LULC types.