esa-sceva/satcom-chunk-collection huggingface dataset:
# Link-State Aware Hybrid Routing in the Terrestrial-Satellite Integrated Network +
## 5 Simulation and Analysis
### Satellite Routing Performance
#### 5.2.2 Packet Drop Rate and Network Throughput  
Apart from data transmission delay, the packet drop rate and network throughput are also crucial metrics for network performance analysis, thus we further adopt them to verify the routing effectiveness. Figure 9 shows the total packet drop rate experienced by all sessions for different sending rates. Obviously, TSR achieves the least packet drop rates among all simulated methods. Since the packet data rate is mainly caused by traffic congestion, thus the performances of these four simulated methods are similar to the end-to-end and queuing delay as shown above. The more data choose the same way for  
Figure 8: Average queuing delay at each satellite.  
Figure 7: End-to-end delay vs. transmission rate.  
transmission, the higher the packet drop rate. The good performance of the TSR method in the lower packet drop rate is also reflected in the high throughput. As shown in Figure 10, the proposed TSR algorithm achieves a better performance in the throughput, the reason is that the TSR algorithm uses the space-time graph model to depict the data transmission process in dynamic TSIN, which avoids the data packet drop caused by ISL disconnection, and the inter-satellite link connection with the minimum delay is obtained, which can achieve load balancing.
# Effects of Biochar Production Methods and Biomass Types on Lead Removal from Aqueous Solution
## 3 Results and Discussion
### FTIR Analysis  
The FTIR results of AST, APT, GPT, and BST biochar samples are shown in Figure 3. Land-based biochars (AST, APT, and GPT) showed a broad peak (3500-3200 cm\({}^{-1}\)) by O-H stretching in varying degree, indicating dehydration of cellulosic and ligneous components [18; 33; 34]. Marine-based biochar (BST) was relatively flat in that region. The land-based biochars showed absorption of the aromatic component (C-H stretching at 3050 cm\({}^{-1}\)) and aliphatic component (C-H stretching at 2935 and 2885 cm\({}^{-1}\)). The land-based biochars displayed the stretching of carbonyl bonds (C=O) at 1740-1700 cm\({}^{-1}\), indicating the presence of carboxylic groups and possibly traces of aldehydes, ketones, and esters [33]. Aromatic C=C stretching (1600 cm\({}^{-1}\)) was also observed in the land-based biochars. The band at 1030 cm\({}^{-1}\) was assigned to C-O-H or C-O-R representing oxygenated functional groups [33], and it was observed in all biochars except APT.  
BST biochar showed unique features in the fingerprint region (<1400 cm\({}^{-1}\) in wavelength). The band at 1047 cm\({}^{-1}\) was assigned to C-O stretching indicating the presence of carbonate and calcium atoms [35]. The broad peak at around 1210 cm\({}^{-1}\) was assigned to C-H bonds of aromatic rings or axial deformation vibrations of C-O bonds in phenols [36]. A sharp bend can be observed at 870 cm\({}^{-1}\), indicating aromatic C-H deformation in the BST [37].
## 3 Results
### Correlation of Flow Velocity in Different Fingers  
To test for the correlation of the measured values in each finger in HCs and SSc patients, we performed a correlation analysis of fingers DII-DV (Figure 8). Correlation between the different fingers was, despite being statistically significant at D II/III in SSc patients and D II/IV and D IV/V in HCs, at best, moderate. The highest correlation in SSc patients for PS and ED flow was observed for the second and third finger (correlation coefficient of 0.58 and 0.65, respectively). In HCs, the highest correlation was observed for PS flow between the second and fourth finger and between the fourth and fifth finger (correlation coefficients of 0.57 and 0.54, respectively).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **All Fingers Combined** & **Minimum** & **Maximum** & **Sum of Values** \\ \hline Sens PS (95\% CI) & 0.69 (0.58–0.78) & **0.90 (0.70–0.98)** & 0.90 (0.70–0.98) & 0.85 (0.64–0.95) \\ \hline Spec PS (95\% CI) & 0.88 (0.79–0.93) & **0.85 (0.64–0.95)** & 0.70 (0.48–0.85) & 0.90 (0.70–0.98) \\ \hline Sens ED (95\% CI) & 0.65 (0.54–0.75) & 0.85 (0.64–0.95) & **0.90 (0.70–0.98)** & 0.80 (0.58–0.92) \\ \hline Spec ED (95\% CI) & 0.80 (0.70–0.87) & 0.80 (0.58–0.92) & **0.95 (0.76–1.0)** & 0.85 (0.64–0.95) \\ \hline AUC PS (95\% CI) & 0.84 (0.77–0.90) & **0.93 (0.85–1.0)** & 0.85 (0.73–0.96) & 0.91 (0.82–1.0) \\ \hline AUC ED (95\% CI) & 0.80 (0.73–0.87) & **0.90 (0.80–0.99)** & 0.92 (0.82–1.0) & 0.89 (0.79–0.99) \\ \hline LR+ PS & 5.5 & **6.0** & 3.0 & 8.5 \\ \hline LR + ED & 3.3 & 4.24 & **18.0** & 5.3 \\ \hline Youden’s J PS & \textless{6.9 cm/s} & \textless{6.13 cm/s} & \textless{12.28 cm/s} & \textless{32.1 cm/s} \\ \hline Youden’s J ED & \textless{2.68 cm/s} & \textless{2.13 cm/s} & \textless{4.45 cm/s} & \textless{12.65 cm/s} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test characteristics and optimal cut-off points for all fingers.  
Figure 7: Receiver operating characteristics curve for PS and ED flow velocities for the minimum values obtained. (**A**) Cut-off and test characteristics for PS flow velocity. (**B**) Cut-off and test characteristics for ED flow velocity. LR+, positive likelihood ratio.
# High-Precision Image Editing via Dual Attention Control in Diffusion Models Without Fine-Tuning
## 5 Experiment
### Ablation Study
#### 5.3.2 Module Qualitative Analysis--Fine Layer  
As shown in Figure 7, if control is not used (column 2), the diffusion model will generate another image instead of completing the image editing. Using only cross-attention control (column 3): When using only cross-attention control, while the specified region can be edited, unnecessary changes also occur in other parts of the source image. This not only alters the edited area, but also causes significant changes in unedited areas, affecting the overall fidelity of the image and failing to meet the high-precision editing requirements. Using both cross-attention control and self-attention control in layers 5-8 (column 5): We found that applying self-attention control in layers 5-8 effectively preserves the details of the source image. This approach is suitable for situations that require precise control over editing details, as these layers can be considered the \"coarse layers\" of self-attention control, helping to retain the fundamental structure and details of the source image.  
\begin{table}
\begin{tabular}{l c c c} \hline
**Module** & **CLIP Score \(\uparrow\)** & **LPIPS \(\downarrow\)** & **FID \(\downarrow\)** \\ \hline No Control & 0.323 & 0.416 & 27.5 \\ Only Cross-Control & 0.328 + 1.55\% & 0.374 + 10.1\% & 22.0 + 20.0\% \\ Only Self-Control & 0.326 + 0.93\% & 0.385 + 7.45\% & 22.1 + 19.6\% \\ Dual Control & 0.343 + 6.19\% & 0.294 + 29.3\% & 20.7 + 24.7\% \\ \hline \end{tabular}
\end{table}
Table 2: Quantitative analysis of the dual attention control proposed by our method.  
As shown in Figure 8, using only cross-attention control (column 3): While style transformation can still be achieved using only cross-attention control, many areas of the image are re-generated in addition to the style change. This gives the impression that an entirely new image with a different style has been generated rather than an edited version of the original image. Using only self-attention control (column 4), the image retains some features of the input image, but cannot perform the desired editing or style transfer. Using both cross-attention control and self-attention control in layers 1-4 (column 5): When editing the style of an image, we recommend using self-attention control in layers 1-4. These layers can be considered the \"fine layer\" of self-attention control, better suited for style transformations. This approach effectively alters the overall style of the image while minimizing the impact on non-edited areas.  
Figure 7: Self-attention control in coarse layers. Using only cross-attention control results in significant changes in detail and background (column 3, for example, the background of the bear changes from a house to a car), whereas using dual attention control maintains detail while enabling the desired editing.
## 2 Experimental Setup  
The experimental setup of the ECDL is given in Figure 1. A commercial single-angled facet (SAF) gain chip (Thorlabs (Newton, NY, USA), SAF1126 heatsink assemble) is chosen to achieve a stable output. The reflectivity of the rear facet (R\({}_{1}\)) is less than 0.01%, which is ultra-low to ensure that the light feedback level reflects back from the external cavity. Additionally, the reflectivity of another facet (R\({}_{2}\)) is about 10%. The laser output from the rear facet is collimated by lens 1 (SBJ2.4F1.14-042), passed through the interference filter (Thorlabs, FBH1550-40) and diffracted on a diffractive grating (Thorlabs, GR13-0616, 600 lines/mm). The interference filter has a high transmission of more than 95%, with 40 nm transmittance ranges from 1525 nm to 1565 nm. The diffractive grating is 600 lines/mm, and the first-order diffractive beam of the grating is feedbacked into the gain chip. Finally, the laser is the output from the front facet, collimated by lens 2 (SBJ2.4F1.14-042) and coupled into a fiber. As seen in Figure 1, the beam splitter (BS) is used to measure the optical power, optical spectrum and linewidth.  
Figure 1: Experimental scheme of the external cavity diode laser is shown. The external cavity is composed of the lens, interference filter and diffraction grating. The laser is output from R\({}_{2}\) and coupled into a fiber to measure the power, optical spectrum and linewidth.  
We employ the delayed self-heterodyne technique to measure the laser linewidth, as shown in Figure 1[17]. The length difference of two interferometer fiber arms needs to be large enough to ensure its accuracy and sensitivity. The output signal first passes the isolator and coupler 1. Then, it is split. One path passes the delayed single-mode optical fiber of 75 km. Another one is sent to the acousto-optic modulator (AOM). To eliminate the influence of DC noise, AOM shifts the laser frequency by 80 MHz. The two beams go through coupler 2 and are mixed by the detector. In addition, the couplers are single-input double-output types and the split ratio is 1:1 for couplers 1 and 2. We obtain the result on the spectrum analyzer and measure the Lorentz linewidth after fitting.
## 4 Data Analysis and Results
### Field Data Analysis  
The length of the Kastelli fault zone exceeds 30 km since it can be traced from the northern coast of Crete to Asteroussa Mt to the south, within the recent sediments of MB. It was mapped and measured at several places, especially where the Alpine basement outcrops hosted the fault plane. It was quite frequent that striations along with other kinematic indicators were overprinted on the calcite coating of the fault plane (Figure 5).  
The fault slip data recorded in the field were used as inputs within the stress inversion method TRM [70] and a stress tensor with S\({}_{1}\): 90/079, S\({}_{2}\): 00/349, and S\({}_{3}\): 00/079 (R = 0.25) was defined. The resolved stress tensor, although calculated with fault planes that do not deviate strongly from each other, is rather compatible with the E-W-trending extension, which in turn agrees with the Arkalochori earthquake focal mechanism.  
During the fieldwork, it was rather obvious that the morphological discontinuity, which the Kastelli fault zone has generated through its activity, is gradually eliminated and finally disappeared within the early Miocene lacustrine sediments of the Viannos, Skinias, and Ampelouzos units [71] and, hence, the fault trace which comprises its southern segment (Lagouta fault, according to [3]) is not very clearly delineated.  
Despite this, a significant number of smaller fault traces that were mapped around Lagouta and Kassani villages (Figure 6a) yield that the Kastelli fault zone continues to the south and ends up to an E-W-trending fault, almost parallel and antithetic to the south Cretan detachment [3]. In addition, a pair of conjugate syn-sedimentary faults was located, measured, and analyzed (Figure 6b) from a kinematic and dynamic perspective. The measurements of the two faults (p1 88/114, p2 72/150) and the sliding directions recorded on them (s\({}_{1}\) 27/025, s\({}_{2}\) 19/066), respectively, were found to be compatible--after tectonic analysis--with left-lateral strike-slip faulting and, specifically, with a stress field which is defined by the axes S\({}_{1}\): 24/046, S\({}_{2}\): 63/200 and S\({}_{3}\): 11/311 [3].  
Figure 5: Fault plane of a Kastelli fault segment measured at 74/263, on which striations measured at 61/220 were found (parallel to the pen), confirming the oblique normal movement with left-lateral component. Measurements at several places are plotted in the inset diagram.  
The left-lateral component of this movement is in agreement with the en-echelon formation of numerous faults found along the previously activated E-W-trending, MB marginal fault zone, at the southern edge of this segment at Asteroussa Mt [3]. The monoclonal stratigraphy of the area yields that the same west-dipping fault zone has also a strong normal component. This conclusion is based on the fact that the outcrops of the older post-alpine sediments (Viannos formation) are found beneath more recent successions that have been mapped in detail on the western fault block and simultaneously at higher altitudes at the eastern one, yielding that the latter has been relatively uplifted (Figure 7).
## 5 General Comparison of the Topologies
Conversely, if the permanent fault is within GS-VSC station, ST, GRT, and WFRT will lose the GS-VSC and hence lack in supplying 400 MW to the connected ac system, while 2 WF-1 SST and 2 WF-SSRT can deal with this situation by providing an alternate path to power flow. However, if the permanent fault persists within extreme end GS-VSC (i.e., GS-VSC\({}_{1}\) or GS-VSC\({}_{3}\)), then again power from WF\({}_{1}\) and WF\({}_{6}\) will be lost for 2 WF-1 SST, while 2 WF-SSRT will aid with overcoming this shortcoming of 2 WF-1 SST by providing intra-connections between units. PPT and ST require the same ratings (400 MW each) of dc cables as their converters. The dc cables of the central ring in GRT demand for power rating equivalent to the total \(P_{\text{WF}}\) (2400 MW). Rating of dc lines within the SS/WF ring is equal to the sum of the power ratings of the two adjacent VSCs i.e., 800 MW while the rating of cables linking WFs to onshore grids needs to be equal to the ratings of WF-VSCs and GS-VSCs, respectively. For 2 WF-1 SST and the proposed 2 WF-SSRT, lines integrating the WFs to the common point needs to be rated at double capacity of the WF side VSCs while the rating of the link (cable) between neighboring WF-VSCs should be equal to the WF-VSC. However, dc cables rating in SS ring of 2 WF-SSRT needs to be equal to the summation of two adjacent VSCs, while dc cables are rated equal to the onshore converter for 2 WF-1 SST. Likewise, no dc breaker is required for PPT. Six DCCBs of 400 MW are needed at the offshore site, while six HVdc breakers of 480 MW are required at the onshore station for star topology. Twelve HVdc circuit breakers are required for the central ring in GRT, each of them being 2400 MW. Six DCCBs are demanded in the SS/WF ring of SSRT/WFRT, each of them 800 MW. The 2 WF-1 SST and 2 WF-SSRT topologies require only three dc circuit breakers, each of them 1200 MW, respectively. The results of the topological evaluation are summarized in Table 3 from Figure 8 and the above discussion. Among them, 2 WF-SSRT topology is the best for WF integration with onshore grids because it gives maximum flexibility, stability, reliability, redundancy, and utilization as it can tolerate all faulty conditions with three DCCB and GS-VSCs, respectively. A VSC power converter station of 1000 MW costs 110 ME, while the cost of DCCB is one-sixth of the power VSC converter price [36; 37]. Subsea HVdc cable approximate price is in the range of 1.2 ME-1.4 ME per km [38]. This information proves that 2 WF-SSRT topology offers reduced overall capital and operating cost (economical) even better than 2 WF-1 SST.
# A Novel Multi-Agent-Based Approach for Train Rescheduling in Large-Scale Railway Networks
## 5 Case Study
### Computational Complexity Analysis
\begin{table}
\begin{tabular}{|c|c|c|} \hline
**Variable or Constraints** & **Asymptotic Complexity of the Whole Problem** & **Asymptotic Complexity (in MAS)** \\ \hline Continuous variable & \(|\mathbb{T}|\cdot|\mathbb{J}|\cdot x\) & \(|\mathbb{T}|\cdot x\) \\ \hline Binary variable & \(\frac{1}{2}|\mathbb{T}|\cdot(|\mathbb{T}|-1)\cdot|\mathbb{J}|\cdot x\) & \(\frac{1}{2}|\mathbb{T}|\cdot(|\mathbb{T}|-1)\cdot x\) \\ \hline Railway & \(|\mathbb{T}|+|\mathbb{T}|\cdot|\mathbb{J}|\cdot x+\frac{1}{2}|\mathbb{T}|\cdot(| \mathbb{T}|+\) & \(|\mathbb{T}|+|\mathbb{T}|\cdot x+\frac{1}{2}|\mathbb{T}|\cdot(|\mathbb{T}|+1) \cdot|\mathbb{S}|+\) \\ operational constraints & \(1)\cdot|\mathbb{S}|+\frac{1}{2}|\mathbb{T}|\cdot(|\mathbb{T}|-1)\cdot|\cdot x+ & \(\frac{1}{2}|\mathbb{T}|\cdot(|\mathbb{T}|-1)\cdot x+\frac{1}{2}|\mathbb{T}| \cdot(|\mathbb{T}|-1)\cdot|\mathbb{S}|+\) \\ \cline{2-3}  & Timetable & \(\frac{1}{4}|\mathbb{T}|\cdot(|\mathbb{T}|-1)\cdot|\mathbb{J}|\cdot(|\mathbb{J}| -1)\cdot x\) & \(\frac{1}{4}|\mathbb{T}|\cdot(|\mathbb{T}|+1)\cdot|\mathbb{J}|\cdot(|\mathbb{J}| -1)\cdot x\) \\ \hline \end{tabular} N.B. \(x\) is the number of train passes at junction areas.  
\end{table} 
Table 1: Asymptotic complexity of the whole problem and each junction agent in MAS. \begin{table}
\begin{tabular}{|p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}|} \hline
**Variable or Constraints** & **Whole Problem** & **North Stafford Jin** & **Derby LNW Jin** & **Derby London Road Jin** & **Derby Station North Jin** & **Ambegar Jn** & **Collaborative Agent** \\ \hline Continuous variable & 149 & 27 & 27 & 4 & 5 & 26 & 0 \\ \hline Binary variable & 2273 & 351 & 351 & 946 & 300 & 325 & 0 \\ \hline Operational constraints & 453 & 459 & 837 & 2104 & 724 & 429 & 0 \\ \hline Timetable coupling constraints & 16,820 & 0 & 0 & 0 & 0 & 0 & 16,820 \\ \hline \end{tabular}
\end{table} 
Table 2: The exact value for computational complexity analysis in the case study. problems using the MAS can not only significantly reduce computational time but also potentially improve the quality of the final solution.
## 3 Relaying  
In this section, we unveil the intricacies of the Relay, focusing on the essential components and resources foundational to its design. This includes an examination of both its hardware and software elements, alongside bespoke developments tailored for its construction and actual deployment. By presenting the Relay's architecture and operationalcapabilities, this section aims to elucidate its role and efficiency within the broader context of IoT applications, demonstrating the Relay's simple yet efficacious functioning.
# Linewidth Measurement of a Narrow-Linewidth Laser: Principles, Methods, and Systems
## 2 Basic Principles
### Delayed Self-Homodyne Method  
The delayed self-homodyne method [27] requires only one tested laser, avoiding strict performance requirements for the reference source. The basic structure of the Mach-Zehnder interferometer (MZI) is shown in Figure 1b. First, the tested laser is split by a coupler, and one beam is propagated through the fiber to eliminate coherence and interferes with the other beam to beat the frequency. The mixed-frequency differential beat optical signal is converted into an electrical signal by a photodetector (PD), and the power spectrum of the current signal is analyzed by an ESA [28].  
The delayed self-homodyne system converts the phase fluctuation of the measured laser into intensity fluctuation, and the optical signal electric field output by the laser is:  
\[E(t)=E_{0}exp\{j[2\pi f_{0}t+\varphi_{0}(t)]\} \tag{5}\]  
where \(E_{0}\) is the intensity of the light field,\(f_{0}\) and \(\varphi_{0}\) are the output frequency and initial phase, respectively. The light intensity electric field of the beat frequency signal is:  
\[\mathrm{E}_{T}(t)=\mathrm{E}(t)+\mathrm{E}(t+\tau_{0}) \tag{6}\]  
where \(\tau_{0}\) is the delay time. The self-correlation function of the light intensity electric field is:  
\[R_{I}(\tau)=\langle\mathrm{E}_{T}(t)\mathrm{E}_{T}{}^{*}(t)\mathrm{E}_{T}(t+ \tau_{0})\mathrm{E}_{T}{}^{*}(t+\tau_{0})\rangle \tag{7}\]  
The simplified PSD function is obtained according to the Wiener-Khinchin theorem:  
\[S_{T}(w)=I_{0}\Big{[}v^{2}+(2/\tau_{c})/(2/\tau_{c})^{2}\Big{]} \tag{8}\]  
where \(\tau_{c}\) is the coherence time. The beat frequency linewidth is twice the linewidth of the laser to be measured, and the measured linewidth is \(\Delta v=1/\tau_{c}\).
# Flavor Chemistry of Virgin Olive Oil: An Overview
## 3 Factors Affecting the Flavor of VOO
### Physico-Chemical Properties of Volatile Compounds  
Generally, high molecular weight flavor compounds are retained in the food matrix more than low molecular flavor compounds. Moreover, the chain length of flavor compounds has an impact on their perception, as long chain length molecules will be retained more than short chain molecules. [PERSON] and co-workers [111] verified that the release of aldehydes and diacetyl in an oil-in-water model system is influenced by the length and unsaturation of compounds. Particularly, the shape, position, and nature of the functional groups of volatile compounds seem to have a strong and significant effect on the nature and strength of the bond with the food matrix and olfactory receptors.  
The hydrophobicity of a molecule, expressed as log P, could regulate the mass transfer from two different liquid phases; therefore, it could be a useful means to understand the retention and/or the release of volatile compounds from olive oil emulsion produced in mouth with saliva. A high log P\({}_{\text{o/w}}\) value indicates that a compound tends to preferentially partition into organic phase rather than water, and it is inversely related to the solubility of a compound in water. [PERSON] and co-workers [112] studied a model system of sunflower oil and reported a \"salting out\" effect for some volatile compounds after the addition of artificial saliva (salts, mucin and \(\alpha\)-amylase). The authors stated that this effect was mainly due to the hydrophilicity of the molecules.
## 6 Design Considerations for LPWAN Networks
### Coverage  
Coverage is a critical attribute for LPWAN-based applications. There are several aspects of coverage--reaching a larger geographical area, areas around obstructions, and areas inside buildings. Techniques to overcome issues associated with degradation of signal include repeated transmission, boosting of transmitter power, improving sensitivity of antennas and transceivers, and decreasing the modulation rate. The last item increases the probability of successful detection. It may be noted that, similar to the scenarios for capacity, weak signals in enhanced coverage also result in additional interference. The techniques for interference management mentioned in handling capacity are also applicable to such situations.  
Repeated transmissions entail sending the same data a defined number of times with the same or different error detecting codes. The receiver processes multiple copies of the packets received and extracts the data information. Power boosting implies increasing the transmission power to provide higher coverage. To handle low levels of signals and increase coverage, antenna configuration may be enhanced, and receiver sensitivity can be increased. It may be noted that, the lower the modulation rate, the higher the probability of detecting bits correctly. However, a lower modulation rate also decreases the spectrum efficiency (bps/Hz.), thus reducing the data bandwidth. This may be acceptable for many applications.