esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments
### Experiment 4: Static Splitting Factors with Stopping Criteria  
The _purpose_ of the current experiment is to assess the effectiveness of our splitting scheme across various static splitting factors while introducing three distinct stopping criteria. Recall from Section 3.3, that the metrics that are used in case a stopping criterion is applied are the _time elapsed_, _resources consumption_, and the _detection rate_, depending on the criterion in force. Particularly, in Section 5.4.1, the simulation is conducted with a time constraint of 15 min. This value is determined by rounding the median time (16.79 min) taken by the time-wise best-performing splitting factor of 10 when considering a pollution factor of 10%. Regarding the time criterion, the meaningful metrics are the _resources consumption_ and the _detection rate_.  
Next, in Section 5.4.2, the simulation is performed with a resource constraint of 1000 replicas. Similarly, this value is derived from rounding the median of replica nodes (920.2 nodes) taken by the resource-wise best-performing splitting factor of 2, when considering a pollution factor of 10%. For the resource criterion, the relevant metrics are the _time elapsed_ and _detection rate_.  
Last, in Section 5.4.3, the simulation is executed with a connection saved percentage constraint of 80%. This percentage was decided from Tables 6 and 7, where for both curves of Figure 9 the critical point is when they have achieved to save approximately 80% of the benign connections, specifically in epochs 11 (10.56 min) and 3 (3.99 min) for splitting factors of 2 and 10, respectively. When the connection saved percentage criterion is applied, the appropriate metrics are the _time elapsed_ and _resources consumption_.
## 4 Discussion
### Key Findings
Although a pioneering evaluative benchmark designed for XAI visualization in general image analysis tasks is noted [54], it lacks a focus on medical or cancer applications and does not consider semantic or example-based XAI. Moreover, evaluations often miss objective judgments and feedback from clinical end-users, which are crucial components for practical implementation. Studies using tailored quantitative metrics such as ZNCC scores focus more on technical validation, while methods involving clinicians ensure that AI aligns with clinical reasoning, stimulating better integration [38]. Only a few studies actively incorporated clinicians in their XAI evaluations [40; 44; 46; 47; 49; 53]. [PERSON] et al. (2021) actively involved physicians in assessing the resemblance levels of the ROE to ensure that AI-generated evidence was clinically relevant and aligned with their diagnostic criteria [40]. Additionally, three studies [44; 46; 49] iteratively consulted clinicians in the development of the XAI, to ensure that explanations were clinically relevant and useful. Clinicians participated in usability testing sessions to evaluate the effectiveness and clarity of the explanations provided by the XAI systems. Thereafter, their feedback was used to refine the explanations to match clinical needs better [46; 49]. [PERSON] et al. (2023) specifically employed a human-centered design to involve healthcare professionals throughout the design, implementation, and evaluation phases of the proposed XAI pipeline [44]. The remaining two studies solely incorporated radiologists [47] or endoscopists [53] to qualitatively assess models' performance in providing understandable visual outputs.
## 3 Validation of Numerical Models
### Numerical Simulation of Push-Out  
According to [PERSON] [56] and [PERSON] et al. [59], the push-out experiment uses an aluminum rod with an ice cylinder adhered around it. Figure 9 displays the sample size and numerical  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material Name** & **Parameter** & **Symbols** & **Value** \\ \hline \multirow{3}{*}{Al} & [PERSON]’s modulus & \(E\) & 73,100 MPa \\  & [PERSON]’s ratio & \(\
u\) & 0.33 \\  & Density & \(\
u\) & 2750 kg/m\({}^{3}\) \\ \hline \multirow{3}{*}{Cohesive behavior (SCB)} & Fracture energy & \(G_{\text{c}},G_{\text{c}},G_{\text{c}}^{\prime}\) & 0.0005 N/mm \\  & Peak reactive force & \(t_{\text{a}n},t_{\text{a}n},t_{\text{b}n}\) & 0.0008 N/ma \\  & Stiffness & \(k_{\text{a}n},k_{\text{a}n},k_{\text{b}n}\) & 100,000 MPa/mm \\ \hline \multirow{3}{*}{Cohesive behavior (Push-out)} & Fracture energy & \(G_{\text{c}}^{\prime},G_{\text{c}}^{\prime},G_{\text{c}}^{\prime}\) & 0.004 N/mm \\  & Peak reactive force & \(t_{\text{a}n},t_{\text{a}n},t_{\text{b}n}\) & 1.7 N/Pa \\ \cline{1-1}  & Stiffness & \(k_{\text{a}n},k_{\text{a}n},k_{\text{b}n}\) & 250 MPa/mm \\ \hline \hline \end{tabular}
\end{table}
Table 2: SCB and Push-out numerical simulation of material parameters [56; 57; 58; 59].  
Figure 8: Debonding process of SCB: (**a**) start of debonding, (**b**) end of debonding.  
Figure 7: Load-displacement relationship curve of SCB [57].  
model. During the experiment, a velocity of 0.02 mm/s was provided to the top of the aluminum rod, gradually pushing it out of the ice cylinder until it was entirely out. The lower surface of the ice cylinder was fixed. A cohesive element is embedded between the aluminum bar and the ice, and a C3D8R is used to simulate the aluminum bar and ice. Table 2 displays the material properties of the cohesive element, ice, and aluminum bar.  
The push-out experiment is modeled using four element sizes: 0.6 mm, 0.8 mm, 1 mm, and 1.2 mm. Figure 10 shows a comparison of numerical and experimental load-displacement relationships. The findings indicate that element size has essentially little effect on calculation results. The simulated and experimental load-displacement curves are often in good agreement. The simulated peak load was 165 N. The load-displacement at peak load was 0.0086 mm, with a relative error of 1.9%. The stress cloud in Figure 11 shows the process of releasing ice from an aluminum bar. Figure 11a,b depicts the cohesive elements from the start of destruction to ultimate disappearance. It simulates the start and completion of debonding in ice cylinders and aluminum rods.  
Figure 10: Load-displacement relationship curve of push-out [59].  
Figure 9: Numerical model of push-out.
# Preliminary Impact Assessment of the Ad Hoc Separation Minima: A New Separation Mode
## 3 Methodology  
Once the CONOPS of the Ad Hoc separation minima has been briefly described, the methodology used for the preliminary impact assessment of the implementation of the Ad Hoc separation mode in a scenario is presented.  
KPAs are defined by ICAO [34] as a way to categorize performance aspects relative to high-level ambitions. Key Performance Indicators (KPIs) quantitatively express current, past, or expected future performance, as well as the actual progress in achieving performance objectives [35]. The difference between KPIs and Performance Indicators (PIs) is that KPIs are those which have an associated Validation Target, while PIs are those that do not.  
Table 1 shows the KPAs and PIs selected for this research. Other studies with similar objectives also used these indicators [36; 37]. It can be seen in Table 1 that there is no metric related to safety. However, it should be noted that the ADSM were determined on the basis of a risk assessment. Each Ad Hoc separation value corresponds to a risk value that was checked against a TLS. Therefore, the values are safe. The following KPAs and PIs were used.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**KPA (SESAR)** & **PI** & **Units** \\ \hline
**Capacity** & En-route throughput &
\begin{tabular}{c} Number of aircraft \\ entering per hour \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: KPAs and PIs defined for the preliminary impact assessment of the ADSM concept.  
Figure 2: LECMZMU Ad Hoc separation minima for specific situations in NM.
###### Abstract  
The effects of ultrasound (US), thermosonication (TS), ultrasound combined with nisin (USN), TS combined with nisin (TSN), and conventional thermal sterilization (CTS) treatments on the inactivation of microorganisms in grape juice were evaluated. TS, TSN, and CTS treatments provided the desirable bactericidal and enzyme inactivation, and nisin had a synergistic lethal effect on aerobic bacteria in grape juice while not having any obvious effect on the mold and yeast. Compared with CTS, the sensory characteristics of grape juice treated with TS and TSN are closer to that of fresh juice, its microbial safety is ensured, and the physicochemical properties are basically unchanged. More importantly, the total phenolic content and antioxidant capacity of juice treated with TS and TSN were significantly increased, and the total anthocyanin and flavonoid contents were largely retained. Taken together, these findings suggest that TS and TSN has great potential application value and that it can ensure microbial safety and improve the quality of grape juice.  
g _foods_: grape juice; thermosonication sterilization; nisin-assisted thermosonication sterilization; microbial safety; sensory quality; functional indicators
# A W-Band Communication and Sensing Convergence System Enabled by Single OFDM Waveform
## 3 Proposed Method for Range Extension
### Signal-to-Interference Ratio  
In the proposed method, the signal-to-interference ratio (SIR) changes with the shifting of \(k\). At the sensing receiver, strength of an echo depends on several factors such as antenna gain, round trip distance from the target, carrier frequency, and radar cross-section, as mentioned in (6). If we define \(P_{\text{Rx}_{1}}\) as the received power of \(y_{1}(k)\), \(P_{\text{Rx}_{2}}\) for \(y_{2}(k)\), and \(P_{\text{Rx}_{3}}\) for \(y_{3}(k)\), then the SIR during the shifting of \(k\) is as below.  
* At the beginning, echoes within \(y_{1}(k)\) are detected under the collective ISI caused by \(y_{3}(k)\) and \(y_{2}(k)\), and we can define the SIR during this process as \[\text{SIR}_{1}=\frac{P_{\text{Rx}_{1}}}{\alpha_{2}(\hat{k})P_{\text{Rx}_{2}}+P _{\text{Rx}_{3}}},\] (30) where \(\hat{k}\) indicates the shift in \(k\) and \(0\leq\alpha_{2}(\hat{k})<1\) defines the part of \(P_{\text{Rx}_{2}}\) appearing as interference. \(\alpha_{2}(\hat{k})=0\) indicates that there are no echoes to form \(y_{2}(k)\).
* At the second stage, when \(\hat{k}\) is beyond the \(N_{\text{cp}}\) and within \(N_{s}\), echoes that form \(y_{2}(k)\) are detected and a part of \(y_{1}(k)\) causes ISI, which increases with \(\hat{k}\). The SIR can be defined as \[\text{SIR}_{2}=\frac{P_{\text{Rx}_{2}}}{\alpha_{1}(\hat{k})P_{\text{Rx}_{1}}+ P_{\text{Rx}_{3}}},\] (31) where \(0\leq\alpha_{1}(\hat{k})<1\) is used to account for the ISI caused by part of \(P_{\text{Rx}_{1}}\).
* Similarly, when we detect echoes in \(y_{3}(k)\), the SIR is \[\text{SIR}_{3}=\frac{P_{\text{Rx}_{3}}}{P_{\text{Rx}_{1}}+\alpha_{2}(\hat{k} )P_{\text{Rx}_{2}}},\] (32) where \(P_{\text{Rx}_{1}}\) appears as ISI because at this stage the element-wise division is performed by the previous OFDM symbol \(S_{q-1}\).  
Here, it is important to mention that \(P_{\text{Rx}_{1}}>P_{\text{Rx}_{2}}>P_{\text{Rx}_{3}}\) (assuming same radar cross-section for different targets associated with echoes) because of the FSPL difference between echoes that form \(y_{1}(k)\), \(y_{2}(k)\) and \(y_{3}(k)\). Therefore, \(\text{SIR}_{1}>\text{SIR}_{2}>\text{SIR}_{3}\), which clearly indicates that detection of echoes in \(y_{2}(k)\) and \(y_{3}(k)\) is not possible without the sufficient processing gain obtained through the IFFT /FFT operation during sensing. Usually, the Doppler estimation requires large interval (compared to the OFDM symbol duration) of the waveform; therefore, large number of OFDM symbols can provide sufficient processing gain for the echoes to overcome ISI.
###### Abstract  
The industrial scenario is indispensable for ubiquitous 6G coverage, which demands hyper-reliable and low-latency communication for full automation, control, and operation. To meet these demands, it is widely believed that it is necessary to introduce not only the conventional sub-6 GHz bands but also high-frequency technologies, such as millimeter wave (mmWave), terahertz (THz), and visible light bands. In this paper, we conduct a channel characterization and comparison in the industrial scenario from the sub-6 GHz to visible light bands. The channel characteristics, including the path loss (PL), root mean square (RMS) delay spread (DS), and angle spread (AS), were analyzed with respect to the frequency dependence and the distance dependence. On the one hand, the visible light band exhibited significant differences in channel characteristics compared to the electronic wave band. Due to the line-of-sight transmission of VLC, the visible light band had a higher path loss, and the path loss exponent reached 3.84. Due to the Lambertian radiation pattern, which has a wide range of reflection angles, the AS of the visible light band was much larger than that of the electronic wave band, which were 1.73 and 0.80 for the visible light and THz bands, respectively. On the other hand, the blockage effect of the metal instruments in the industrial scenario will greatly affect the channel characteristics. As the transceiver distance grows large, signals from both sides of the receiver will be blocked by metal instruments, resulting in a decreasing trend in the RMS DS for the electronic wave band. Moreover, the statistical characteristics of the channel properties were modeled and compared with the 3 GPP TR 38.901 standard. It was found that the height of the receiver caused the difference between the proposed model and the 3 GPP model and needs to be taken into account when modeling. Furthermore, we extended the 3 GPP model to the THz and VLC bands and provided the statistical parameters of the channel characteristics for all frequency bands. This study can provide insights for the evaluation and standardization of multi-frequency communication technology in the industrial scenario.  
chapter 2025, 12, 257
## 4 The Bassnet Model
### Temporal Multi-Resolution  
The amount of computer memory that is needed to pass data and gradients through the model is proportional to the window size, and large window sizes may have prohibitive memory requirements. To accommodate for larger window sizes, we use what we call temporal multi-resolution, a lossy compression of the data into multiple channels of smaller size than the original data. This technique consists in repeated downsampling and cropping of data windows along the time axis with increasing downsample ratios, in such a way that the data in the center of the window are kept at full resolution, and the data are stored with decreasing resolution towards the outer parts of the window. The data at different resolutions are cropped in such a way that the results have equal length, and can thus be stored alongside each other as separate channels in a tensor. Figure 2 illustrates the process.  
Note that the total number of elements in the compressed data window scales logarithmically with the number of elements in the original data window. The example presented in Figure 2 yields a modest reduction from 16 elements to 12 elements, but note that following the same procedure a window of 128 elements is compressed to 24 elements. Furthermore, note that the compressed window size can be varied. In the given example, it is set at 4, which for an input of size 16 yields \(\log_{2}(\frac{16}{4})+1=3\) channels. A compressed window size of 2 would encode an input size 16 into \(\log_{2}(\frac{16}{2})+1=4\) channels, and compressed size of 8 would yield \(\log_{2}(\frac{16}{8})+1=2\) channels. When specifying the window size in a multi-resolution setup we specify both the compressed and the expanded (i.e., original) window size. For example, the multi-resolution window in Figure 2 would be specified as 4/16.  
The expansion step that is shown at the bottom of Figure 2 is used for combining the outputs of the model for consecutive windows. We expand the compressed window by upsampling the downsampled channels and substituting the center part of the upsampled data by the data of the higher resolution data. The expanded data are then windowed and used in an overlap-and-add fashion in order to produce a full track (not shown in the figure). We use an even-sized non-symmetric Hann window, because it allows for adding overlapped windows without rescaling when using a hop size of half the window size. The impact of the low-resolution parts of the expanded window is limited, because the windowing weights the center of the windows stronger than the sides.
## 3 Literature Review and Comparison of Interferometric Coherence and Intensity Correlation
### Combination of Interferometric Coherence and Intensity Correlation and Comparison of Both
Due to their different behavior, both interferometric coherence and intensity correlation have their advantages: The coherence provides additional independent information which cannot be detected from optical imagery, _i.e._, the coherence of a pre-post-event SAR image pair is useful information to support damage analysis derived from optical data. However, short spatial and temporal baselines are required (_cf._ Section 4). On the other hand, at larger spatial baseline cases, SAR intensity correlation still provides useful information on changes on the ground. As reference for the validation of the damage assessment procedure, the authors derived different land cover classes (including damaged and undamaged areas) from optical imagery of the Indian Remote Sensing (IRS) satellite. The authors reported an overall accuracy (and Kappa coefficient) of _ca._ 49% (0.35), 55% (0.38) and 60% (0.46) for the application of damage assessment methods based on the coherence, the SAR intensity correlation and the combination of both methods together with the analysis of the SAR intensity, respectively. [PERSON] [106] compared SAR intensity correlation and coherence for the assessment of damage and reported slightly higher sensitivity of coherence to detect changes on the ground. According to [PERSON] [106], the differentiation into different levels of damage is very hard; valid for both intensity correlation and coherence. [PERSON] _et al._[8] applied both interferometric coherence and intensity correlation for damage assessment after the 2003 Bam Earthquake in Iran by using ENVISAT ASAR imagery. As reference for the validation a damage distribution map based on ground observation was used. The authors found better results for coherence differences (overall accuracy: 45%) than for absolute amplitude changes (overall accuracy 41%). The combination of both aforementioned techniques increased the overall accuracy to 52%. Summarizing Section 3.3, the most important statements are (I) combination of different methodologies strongly increases the accuracy of the damage assessment (e.g., [8, 9, 78, 106]) and (II) coherence and SAR intensity correlation provide different kind of information. The former is better suited for the analysis of smaller changes (e.g., better differentiation of slightly damaged and undamaged areas), while the latter is more sensitive to larger changes on the ground (_i.e._, higher damage levels) and is also less influenced by the spatial baseline of the SAR image pair.
# Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning
## 3 Materials and Methods
### Object Detection Model  
For the Faster R-CNN with Inception v2, the parameters used were as follows. An initial learning rate of 0.001 was used and then reduced by a decay of 0.95 every 10,000 steps. The learning rate was in line with that used by previous studies on pavement distress images [58] whilst other rates were experimented with but this rate proving to be effective. A decay was also utilized for the learning rate which helps the model develop momentum and create a quicker convergence as well as reducing opportunities for overfitting with one constant rate. The input images were also resized to \(300\times 300\) pixels. For the SSD using Inception V2, an initial learning rate of 0.002 was used and then reduced by a decay of 0.95 every 10,000 steps. The same approach of a time decay for the learning rate was utilized. The input images were also resized to \(300\times 300\) pixels. The same hyperparameters were established for the SSD using the MobileNetv2 model for comparative purposes. These configurations were set within the configuration files of each prebuilt model.  
Figure 10: The architecture of SSD model.  
Figure 9: The architecture of Faster R-CNN model.
## 4 Proposed Multi-Feedback Interference Cancellation  
Motivated by the algorithm presented in Section 3, we discuss the basic idea of multi-feedback interference cancellation in this section and compare it with the conventional soft interference cancellation and MAP detector via LLR representation. This implies that the proposed multi-feedback interference cancellation algorithm can be considered as a hybrid detection method performing multiple ICI suppression operations simultaneously for the desired subcarrier.
## 5 Experimental Results and Analysis
### Comparison Overview
Figure 7 illustrates the comparison results and variation trend with respect to the duration changing in an intuitive way. It is seen that our CWT-CNN model outperforms the other methods under all different anomaly durations with respect to the evaluation criteria of accuracy, sensitivity, precision, and F1 score. Moreover, it can be observed that as the anomaly duration decreases from 200 samples to 50 samples, the detection performance of MIDS quickly drops, while our proposed model remains almost unchanged, with a slight decrease. As is shown in Table 11, at the shortest duration, our model can achieve a high F1 score of 94.44% compared with the 83.67% F1 score of the MIDS approach. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Acc** & **Sens** & **Prec** & **F1** \\ \hline MIDS & 92.31 & 93.56 & 91.28 & 92.41 \\ RNet & 94.74 & 93.25 & 96.11 & 94.66 \\ CWT-CNN & 96.03 & 95.03 & 96.97 & 95.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison with other methods under the fixed anomaly duration. Figure 6: Detection performance comparison of our proposed model with MIDS and RNet.
## 2 Materials and Methods
### Coded Excitation Based Multi-Line Transmit (MLT) Imaging Schemes  
Using the two sets of orthogonal codes, a 2, 4, and 6-MLT imaging scheme were set up. The MLT transmits were obtained by superimposing the transmit pulse patterns that would be applied to the probe elements to generate subsequent transmit beams [8]. The imaging sector was set to 90\({}^{\circ}\) and covered by 180 image lines. For a given MLT imaging scheme, an orthogonal code pair was applied on neighboring MLT beams in an interleaved manner, as illustrated in Figure 5, i.e., neighboring sub-sectors were scanned using MLT beams with orthogonal codes. In this way, signals generated from neighboring MLT beams were expected to be better differentiated through decoding in reception, which was performed before beamforming.
# High-Speed Directly Modulated Laser Integrated with SOA
Utilizing the SOA's quasi-high-pass filter characteristics, the fabricated DML achieves a bandwidth of 25 GHz, representing a 7 GHz enhancement compared to the 18 GHz bandwidth of non-integrated SOA lasers. The laser outputs over 25 mW at a 60 mA bias current. In back-to-back (BTB) 26.5625 Gbps NRZ transmission tests under limited receiver bandwidth, clear eye diagrams were obtained, and TDEC comparisons show the fabricated DML has superior signal transmission capabilities. Table 1 compares the proposed design with other monolithically integrated SOA-based DMLs, highlighting differences in modulation bandwidth and output power performance.