esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Commercial Availability of Photonic Devices  
The commercial availability of photonic integrated devices is rapidly expanding, with numerous companies such as Intel Corporation [50], Cisco Systems [51], Infinera Corporation [52], and NeoPhotonics Corporation [53] among others developing and manufacturing PICs for various industries. From established players to startups, a growing ecosystem of suppliers is driving innovation and accessibility in the photonic integrated device market, paving the way for widespread adoption and further advancements in photonics technology. The global PIC market has experienced a remarkable surge in recent years, propelled by a confluence of factors including escalating environmental consciousness, governmental incentives, and relentless technological progress. This burgeoning market not only offers promising prospects for various stakeholders such as optical fiber communication and sensors but also underscores the significance of collaborative efforts between private enterprises and governments. By fostering synergy in policymaking, research and development initiatives, and investment strategies, this collaboration can expedite the growth trajectory of the PICs market. Moreover, the burgeoning consumer demand further amplifies the avenues for market expansion, accentuating the need for innovative solutions and enhanced product offerings. Despite a robust growth of 26.2% in 2021, the World Semiconductor Trade Statistics (WSTS) revised the projection downward to single-digit growth for the global semiconductor market in 2022, amounting to a total valuation of USD 580 billion, reflecting a still notable increase of 4.4% [54]. This adjustment underscores the dynamic nature of the industry and highlights the importance of adaptive strategies in navigating market fluctuations.
## 3 Results and Discussion
### Chemical Oxygen Demand
#### 3.2.2 Reduction in COD  
DAF provides a 75.15 \(\pm\) 3.95 % reduction in COD, and this is statistically the same when compared to 200 kDa and 20 kDa pore sized membranes in membrane filtration experiments. The membranes have a higher standard deviation than the DAF because the feed COD changes with time, but the permeate COD - in our results - remained constant in membrane filtration. It was found that 200 kDa and 20 kDa pore sized membranes in membrane filtration experiment are no more efficient than the DAF, but they are more consistent than the DAF. From Figure 6, it is observed that the percent COD reduction increases with a decreased pore size. Among all different treatment methods, the RO membrane in post-DAF membrane filtration provided the highest percent reduction (98.83 \(\pm\) 0.21 %). Another important observation is that the difference in percent reduction between the two experiments for a particular membrane reduces as the pore size of the membrane decreases. In cases where high percent reductions in COD are required and a smaller pore sized membrane like a 0.083 kDa or a 0.058 kDa cut-off membrane was chosen, it is better to choose the membrane filtration experiment instead of the post-DAF membrane filtration experiment, since the percent COD reductions are not significantly different between the two experiments in those two membranes, and it is economically advantageous to run one treatment method instead of two in series. Therefore, the larger pore sized membranes--200 kDa, 20 kDa, 8 kDa and 4 kDa--are selected for fouling studies, since they operate at lower pressures, which means lower costs than the smaller pore sized membranes--0.083 kDa and  
Figure 5: Chemical Oxygen Demand (COD) of the feed and permeate stream of different membranes in (**A**) membrane filtration and (**B**) post-DAF membrane filtration experiment.  
0.058 kDa. Percent reductions in COD were calculated using Equation (1). Fouling rate is the most important parameter in the model as it depicts the rate at which the membrane fouls, and determines the frequency of cleaning, and henceforth, replacement.
## 2 Materials and Methods
### Hyperspectral Image Processing  
Regions of interest (ROIs) for the model development (ROIs-model) were selected from the bone side contaminated images from the training set. Additional ROIs for the model evaluation were further selected from the skin side contaminated images of training set and both sides of the contaminated images from the test set. For the ROIs-model, both meat groups and FMs were selected, while additional ROIs consisted only of FMs. All ROIs were manually defined and created separately one by one by using the ROI tool in ENVI 4.8 (Exelis Visual Information Solutions, Boulder, CO, USA). The ROIs were created with geometric shapes, such as polygons and rectangles, that varied in size.  
All spectral data on each ROI-model were extracted with a custom MATLAB program and averaged into mean spectra. A total of 150 ROIs were selected for the FM group. A total of 40 ROIs were selected for the meat group. The ROIs for the meat group were taken from various locations to cover the variations in spectral responses. Ten ROIs among the 40 meat ROIs were selected from the fatty areas. After these 190 ROIs for  
Figure 2: Chick breast fillets contaminated (**a**) with 5 \(\times\) 5 mm\({}^{2}\) pieces of FMs and (**b**) with 2 \(\times\) 2 mm\({}^{2}\) pieces of FMs.  
each modality were created, the mean spectra from each ROI were obtained. Finally, a spectral matrix of 190 samples \(\times\) 467 bands was constructed from the VNIR images, and 190 samples \(\times\) 198 bands were constructed from the SWIR images. The mean spectral data obtained from the ROI analysis were further processed spectrally. We applied several spectral pretreatment techniques to the data. The pretreatment methods were Savitzky-Golay (SG) smoothing (with 4 th order polynomial within a moving window of 25 for VNIR and a window of 7 for SWIR), Gap Segment 2 nd derivative (Gap and Segment of 7 for VNIR and 3 for SWIR), and standard normal variate (SNV), sequentially. The different moving window size, Gap, and Segment were applied for VNIR and SWIR due to the difference of spectral resolutions (VNIR: 1.27 nm and SWIR: 7.40 nm). This process was performed to remove noise, emphasize the sharp peaks, and correct the scattering effect in spectral data without compromising the integrity and accuracy of the spectral information. The pretreatment methods were applied to both the VNIR and SWIR spectral data, respectively. In the end, the endpoints of the data were excluded (18 data points at both ends of VNIR data, and 7 data points at both ends of SWIR data). All spectral data pretreatment operations were implemented with MATLAB R2020b software (The MathWorks, Inc., Natick, MA, USA).
## 4 Model
### Reduce Coupling  
To prevent over-coupling in edge embeddings, we employ the method of dynamic edge attention pooling [27]. This approach dynamically captures the importance of different edges based on the graph's structure, thereby enhancing the representational capability and performance of the graph neural network. The process is illustrated in Figure 8.  
The input graph is represented as \(\mathrm{G}=(\mathrm{V},\mathrm{E})\), where \(\mathrm{V}\) denotes the set of nodes and \(\mathrm{E}\) denotes the set of edges. For each edge, a corresponding attention weight representing the importance of the relationship between node \(i\) and node \(j\) is defined. This relationship importance is dynamically computed based on the features of the nodes, rather than being pre-defined. The formula for computing the edge attention score is described as follows:  
\[e_{ij}=\mathrm{LeakyReLU}\Big{(}\mathbf{w}^{\top}\big{[}h_{i}\big{|}|h_{j} \big{]}\Big{)}, \tag{7}\]  
where \(h_{i}\) represents the set of neighboring nodes of node \(i\), \(|\) denotes the concatenation operation, \(\mathbf{w}\) is the learned weight matrix, and LeakyReLU is the activation function used to enhance the model's nonlinear fitting capability. Based on the computed edge attention scores \(e_{ij}\), the calculation of edge attention weights can be performed as follows:  
\[\alpha_{ij}=\frac{\exp\big{(}e_{ij}\big{)}}{\sum_{k\in N_{i}}\exp(e_{ik})}, \tag{8}\]  
where \(N_{i}\) represents the set of neighboring nodes of node \(i\). Once the edge attention weights are obtained, feature aggregation can be performed on the corresponding edges as follows:  
\[\widetilde{\mathrm{h}}_{i}=\sigma\Big{(}\sum_{j\in N_{i}}\alpha_{ij}\mathbf{W }h_{j}+\mathbf{b}\Big{)}, \tag{9}\]  
where \(\mathbf{W}\) and \(\mathbf{b}\) are learned parameters and \(\sigma\) represents the activation function. Finally, graph-level pooling is performed to obtain the final pooled graph representation as follows:  
\[\mathrm{h}_{\mathrm{graph}}=\mathrm{AGG}\bigg{(}\bigg{\{}\widetilde{h}_{i} \bigg{|}i\in\mathrm{V}\bigg{\}}\bigg{)}, \tag{10}\]  
where \(\mathrm{AGG}\) represents the aggregation function. By performing dynamic edge attention pooling operations, we can effectively aggregate information from various edges in the graph and assign weights based on their importance. This approach reduces the over-coupling of edge embeddings, resulting in an improved graph representation.  
Figure 8: Over-coupling phenomenon of edge embedding, using dynamic edge attention pooling to reduce over-coupling.
# Damage Identification Based on Adding Mass for Liquid-Solid Coupling Structures
## 5 Damage Identification Experiment of a Liquid Storage Tank
### Developments Finite Element Model
#### 5.3.3 Effect of Immersion Dpth on Finite Element Response  
To analyze the influence of immersion depths on the finite element responses of the tank, experimental and numerical analysis is conducted. Immersion depth values are those used for the cantilever beam example. The first two natural frequencies measured experimentally and those computed from FE simulation are compared in Table 3.  
It can be seen from Table 3 that the first two natural frequencies decrease with the increase of the immersion depth. The maximum error between the theoretical and experimental natural frequencies is less than 5% for different immersion depths, which shows the accuracy of the FE model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Natural** & **1** & **2** & **3** \\ \hline Measured & 146.78 & 194.41 & 357.21 \\ computed & 143.85 & 197.43 & 351.17 \\ Error & 2.00\% & 1.51\% & 1.69\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: A comparison of the first three natural frequencies (Hz) of the tank measured experimentally and computed via the finite element model.  
Figure 23: Natural frequency domain response of acceleration and pulse load.
## 3 Methods  
### Concept  
Three techniques (GC monitoring, SC, and intercomparison) were combined for monitoring the calibration status of operational S-band radar \(Z_{\rm H}\) in real-time, as shown in Figure 2. The location and intensity of the GC \(Z_{\rm H}\) (\(Z_{\rm C}\)) near the radar were stationary and not subject to refractivity or beam propagation. In other words, the distance from radar and the returned power from the GC are constant in the radar equation. The only need-to-know factor for radar calibration is the radar constant which aggregates every effect related to the radar system. However, the change in \(Z_{\rm C}\) (\(\Delta Z_{\rm C}\)) over time can capture the change in radar system status even if the external source is needed to know the radar constant. Hence, the change in the calibration bias of \(Z_{\rm H}\) can be monitored from the \(Z_{\rm C}\), but it is only a relative calibration. The SC method can calculate the absolute calibration bias using polarimetric observations. Unfortunately, it is impossible to monitor \(Z_{\rm H}\) during the non-precipitation period because SC is valid for rain echoes. Since the GC is an ever-present echo, the calibration bias can be estimated from the \(\Delta Z_{\rm C}\) and the calibration bias calculated from the SC method regardless of weather conditions. Therefore, the combination of GC monitoring and SC is an effective countermeasure for monitoring the calibration bias of \(Z_{\rm H}\) in real-time. In terms of the radar network, a comparison of \(Z_{\rm H}\) between adjacent radars can be used to examine the consistency of \(Z_{\rm H}\). If two radars are well calibrated, then the  
mean bias of \(Z_{\rm H}\) is 0.0 dB. The mean bias can determine the calibration bias if one or more radars are well-calibrated over the radar network. Also, the calibration bias estimated from SC can be evaluated by calculating the mean bias of \(Z_{\rm H}\) between adjacent radars. It is noted that the combined method can monitor, calculate, and evaluate calibration bias.
# Extracting Sentence Embeddings from Pretrained Transformer Models
## 5 Conclusions
We did not find token aggregation and post-processing techniques to change such profile curvature. In this research, we used a pre-trained BERT as a manageable representative of transformer models, but the findings should be transferable to other types of transformers, including large language models. We also specifically did not perform task-specific fine-tuning of the model to keep it universal. This enables the same model to be used in multiple ways. For example, given a prompt, its text-level embedding can be extracted from the model using the techniques investigated here, and this embedding can be used to search an external database for related information to add the prompt to the same model, i.e., we can use the same model for both query encoding and generation in retrieval-augmented generation [232]. Alternatively, one of the simpler baseline models proposed here could be used as the query encoder. Conceptualization, L.S. and M.L. ; methodology, L.S. and M.L. ; software, L.S. ; validation, L.S. ; formal analysis, L.S. and M.L. ; investigation, L.S. and M.L. ; resources, M.L. ; data curation, L.S. ; writing--original draft preparation, L.S. ; writing--review and editing, M.L. and L.S. ; visualization, L.S. ; supervision, M.L. ; project administration, M.L. ; funding acquisition, M.L. and L.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Publicly available datasets were used in this study. The details and links to the datasets and preparation codes are presented in Sections 3.5.1-3.5.3 for the clustering, STS, and classification tasks, respectively. The authors declare no conflicts of interest. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Model** & **T0** & **T4** & **BERT** & **BERT + Avg.** & **Avg.** & **B2S** & **B2S-100** & **RE** \\ \cline{2-9}
**Layer** & \multicolumn{3}{c}{**Last**} & \multicolumn{3}{c}{**First + Last**} & \multicolumn{3}{c}{**-**} \\ \hline avg. & 53.9 & 54.5 & 54.6 & 55.4 & 51.7 & 55.9 & 61.2 & 48.2 \\ + normalize & 53.9 & 54.5 & 54.6 & 55.4 & 51.7 & 55.9 & 61.2 & 48.2 \\ + quantile-u & 57.8 & 60.1 & 58.1 & 60.0 & 58.2 & 53.5 & 62.9 & 52.3 \\ + quantile-u & 55.6 & 57.1 & 57.0 & 59.4 & 58.1 & 53.5 & 62.2 & 52.1 \\ + whiten & 64.0 & 65.0 & 66.2 & 68.1 & 69.4 & 67.7 & 68.5 & 68.3 \\ + whiten & 53.5 & 57.0 & 57.6 & 61.1 & 64.5 & 60.7 & 61.3 & 64.6 \\ + zscore & 58.6 & 61.3 & 58.7 & 61.2 & 59.6 & 55.7 & 61.3 & 53.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Spearman correlation dependence on token aggregation and post-processing techniques for semantic textual similarity STS14 task. The best result for each model is bolded, while underlined result is the best across all the models.
## 5 Conclusions
### Organic Combination of Greenhouse Production and Photovoltaic Power Generation  
The coverage rate of roof photovoltaic modules used for the photovoltaic greenhouse in this experiment has reached 76.9%, which showed obvious scale effects of power generation. Meanwhile, the planting of various leaf vegetables also showed good crop production. For example, the yield of pakchoi after planted for 35 days (planted in October-November) can reach 2.27 kg/m\({}^{2}\), which means that the yield per mu can reach 1211 kg/mu when we assumed the actual utilization rate to be 80%. Obviously, this yield is higher than the average level of 750-1000 kg/mu for open-field planting in the area [20]. The planting test showed that the photovoltaic greenhouse can basically meet the normal production of leaf vegetables, which laid a feasibility basis for the realization of photovoltaic generation and agricultural production in tropical areas at the same time.  
The tests were not continuous due to experimental condition limitations. The light intensity at the time and weather when we made the tests was bad in the area. For example, the summer tests were conducted mostly in thundershower weather. No data in sunny days were obtained. While in winter, the tests were conduced in limited rain when the daylighting was very weak. However, the findings of this paper can provide useful data for the further improvement of design and promotion. Future studies can focus on the structural optimization by combining simulation software and actual measurement data, make more reasonable environmental parameter test collection schemes, engage more test varieties and systematically test and analyze the growth cycles, quality, and yield of vegetables.
# Recognition and Location Algorithm for Pallets in Warehouses Using RGB-D Sensor
## 4 Results  
### Template Matching Algorithm Comparison  
The results of the experiment are shown in Figure 7. Both the OpenCV algorithm and the proposed algorithm recognized the pallet when the angle between the sensor and pallet was in the range 0\(-\)10\({}^{\circ}\). However, as the angle increased, the OpenCV algorithm occasionally failed to detect the pallet. When the angle was larger than 15\({}^{\circ}\), the OpenCV algorithm drifted and lost the target but the proposed algorithm was able to identify the pallet (see Figure 7a,d). Moreover, the OpenCV template matching algorithm could have been influenced by the background and blocked by obstacles, as shown in Figure 7b,e. The proposed algorithm was able to resist the impact of the environment and change in pallet, as shown in Figure 7c,f.  
Figure 7: Comparison of experimental results under typical situations. The blue box represents the results of the recognition algorithm. (**a**) Recognition result with the template matching algorithm  
provided by OpenCV when the pallet angle was 15\({}^{\circ}\). **(b)** Recognition result with the template matching algorithm provided by OpenCV when the pallet was subject to environmental influence. **(c)** Recognition result with template matching algorithm provided by OpenCV when the pallet was blocked by obstacles. **(d)** Recognition result with the proposed algorithm when the pallet angle was 15\({}^{\circ}\). **(e)** Recognition result with the proposed algorithm when the pallet was subject to background disturbance. **(f)** Recognition result with the proposed algorithm when the pallet was blocked by obstacles.
###### Abstract  
Electrocardiogram (ECG) analysis is important for the detection of pace pulse artifacts, since their existence indicates the presence of a pacemaker. ECG gives information on the proper functionality of the device and could help to evaluate the reaction of the heart. Beyond the challenges related to the diversity of ECG arrhythmias and pace pulses, the existence of electromyogram (EMG) noise could cause serious problems for the correct detection of pace pulses. This study reveals the potential of a methodology based on Stockwell transformation (S-transform), subsequent Shannon energy calculation and a threshold-based rule for pace artifact detection in a single-lead ECG corrupted with EMG noise. The design, validation and test are performed on a large, publicly available artificial database acquired with high amplitude and time resolution. It includes various combinations of ECG arrhythmias and pace pulses with different amplitudes, rising edges and total pulse durations, as well as timing that corresponds to different pacemaker modes. The training was done over 312 (ECG + EMG) signals. The method was validated on 390 clean ECGs and independently tested on 312 (ECG + EMG) and 390 clean ECGs. The achieved accuracy over the test dataset was Se = 100%, PPV = 98.0% for ECG corrupted by EMG artifacts and Se = 99.9%, PPV = 98.3% for clean ECG signals. This shows that, despite EMG artifacts, the S-transform could distinctly localize the pace pulse positions and, together with the applied ShE, could provide precise pace pulses detection in the time domain.  
pacemaker; pace artifact detection; ECG; EMG noise; Stockwell transformation; S-transform; Shannon energy +
Footnote †: journal: Applied Physics Letters  
Article
# Method of Calculating Desynchronization of DVB-T Transmitters Working in SFN for PCL Applications
## 4 Transmitter Localization in Passive Radars  
As mentioned in previous sections, the precise localization of illuminators of opportunity in passive radars is of utmost importance. Any inaccuracy is propagated onto target localization. If the positions of the ellipses' focal points (Rx and Tx) are uncertain, the whole ellipse position may vary. In extreme cases, when two or more ellipses cross on sections nearly parallel to each other, a slight ellipse shift may result in a disproportionately large target localization error, or the rejection of the measurement.  
There are many databases provided by broadcast infrastructure operators, government agencies, and noncommercial websites, although transmitters' positions are frequently inaccurate, out of date, or simply missing. In order to validate data acquired from external sources, it is possible to use a TDoA-based algorithm [15]. Since passive radars commonly consist of more than one receiver [17], in most cases, no additional hardware is needed to gather data for TDoA processing.  
Figure 2: Passive radar geometry: (**a**) for a single ellipse (bistatic configuration), Rx (receiver) and Tx (transmitter) are ellipse foci, and the sum of \(\mathrm{R_{R}}\) and \(\mathrm{R_{T}}\) distances is constant; (**b**) by adding more ellipses (multistatic configuration), one can find target localization.
## 2 Materials and Methods
### Image Preprocessing and Data Augmentation
Expanding a segmentation dataset is straightforward by applying simple transformations to images and their masks, thereby enhancing data utilization efficiency [40]. In this case, 2242 new images were generated from the initial set of 3200 training images. Among these, 65% are horizontally flipped, 55% have applied zoom and small rotations, 40% exhibit color variations, and 30% have per-pixel Gaussian noise added.