esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Relevant Works  
In the past few years, there have been numerous contributions within the realm of integrated networks that combine FSO and RF technologies with satellite and aerial components, e.g., [13; 14; 15; 16; 17; 18; 19; 20; 21]. In [13], an analytical expression for the outage probability (OP) in a SAGIN has been presented, taking into account the impact of pointing errors in the satellite-aerial segment. In [14], based on the selective decode-and-forward (DF) protocol, the ergodic capacity of a multiuser downlink SAGIN has been analytically studied. In [15], the performance and the coverage of a communication system employing FSO-RF transmission is enhanced by employing a dual-hop configuration with a reconfigurable intelligent surface (RIS). In [16], a low-altitude platform (LAP)-aided dual-hop relaying system with non-orthogonal multiple access (NOMA) and FSO/RF communication has been explored. In this framework, an analysis has been provided that includes outage probability and asymptotic considerations, revealing that LAP altitude and FSO channel parameters significantly impact system performance. In [17], the assessment of a dual-hop hybrid FSO/RF SAGIN has been analytically investigated using the criteria of OP and bit error probability. In [18] an HAPS-selection scheme was introduced in a cooperative SAGIN communication scenario. This scheme was based on a signal-to-noise ratio (SNR) criterion, while the OP analysis that was presented also took into account various impairment effects, including atmospheric turbulence and pointing errors.  
In [19], the utilization of a LEO satellite was explored in order to enhance the performance of two mixed FSO/RF HAPS-assisted communication systems. Moreover, Ref. [20] focuses on a hybrid FSO/RF and SAGIN, in which the OP and the average symbol error probability were investigated, also taking into account various propagation phenomena such as turbulence and weather effects. Finally, in [21], the performance of hybrid FSO/RF relay systems in a satellite terrestrial integrated network was investigated, and the effect of weather conditions was also taken into consideration. In that study, three different schemes were designed on HAPS, while reconfigurable intelligent surface (RIS)-assisted UAVs were also considered. It is noted that in most of the aforementioned studies, valuable insights were provided based on the asymptotic expressions that were also provided. Moreover, another parameter that is very important for the performance of hybrid RF/FSO SAGIN is signaling overhead. In particular, for the various network operations that frequently take place in these systems, e.g., handover and link switching, signaling exchanges between the network nodes should be made. However, this signaling is responsible for latency increase and effective capacity reduction. Therefore, algorithms that efficiently achieve a trade-off between signaling overhead and system performance should be proposed [22; 23].
# Towards Optimizing Multi-Level Selective Maintenance via Machine Learning Predictive Models
## 4 Proposed Methodology
### Predictive Model for Multi-Level Selective Maintenance Using ANN and SVR
#### 4.2.4 Predictive Model on the FMS Data
This observation is entirely consistent with the results obtained in Table 2, where all accuracy evaluation indices are in favor of the ANN model. Thus, the results obtained revealed that the ANN is more suitable for predicting the degradation coefficient than the different SVR models. Moreover, the constructed model can be used to predict the system's actual age based on its maintenance data. \begin{table}
\begin{tabular}{c c c c} \hline
**Prediction Model** & **MSE** & **MAE** & **MAPE** \\ \hline SVR with linear Kernel & 0.19 & 0.15 & 0.3 \\ \hline SVR with RBF Kernel & 0.17 & 0.14 & 0.28 \\ \hline ANN & **0.13** & **0.1** & **0.23** \\ \hline \end{tabular}
\end{table} 
Table 2: Results of performance metrics comparing ANN and SVR models. Figure 2: Actual and predicted degradation coefficient with SVR-RBF Kernel.
# Novel Materials for Heavy Metal Removal in Capacitive Deionization
## 4 Challenges and Opportunities  
Heavy metal removal using CDI techniques provides an energy-efficient and cheap removal method. This real application can be integrated into renewable energy systems, which would be useful in remote areas. Studying the literature on the assessment of CDI energy efficiency reveals its relatively low, specific energy consumption, which is considerably below 1 kWh m\({}^{-3}\). Theoretically, energy is stored in the electrical double layer during the charging process, whereby it may be discharged and recovered. However, practically, resistance during charge and discharge leads to energy losses. This resistance arises from the contact resistance of components, connecting wires, the electronic resistance in the solid matrix of the electrode, and ionic transportation resistance. Hence, this requires the optimization of electrode material composition, structure, and porosity as well as minimized contact resistance in the whole CDI assembly. The pursuit of intercalation materials as promising CDI electrode materials is primarily driven by their ability to yield far higher specific adsorption capacities than conventional carbon-based electrodes. This is due to the ability of intercalation materials to store ions in their solid phase, in contrast to carbon electrodes that store ions by forming EDLs in their micropores. In theory, intercalation materials do not possess a direct advantage over carbonaceous materials considering the reduction in cell resistance and resistive energy loss. Most intercalation materials have poor electronic conductivity, which is inimical to high energy efficiency. However, these materials have two major advantages that allow im-CDI systems to operate in ways that significantly benefit TEE. The first advantage is their high specific adsorption capacity. Although specific adsorption capacity is not directly relevant to TEE, the high SAC of intercalation materials enables im-CDI to remove a considerable amount of salt and thereby achieve an appreciable salinity percentage reduction when employed to desalinate high-salinity feed solutions. CDI requires an electrode with a large ion-accessible specific surface area. Metal ion electrosorption is a surface-related process, and surface area plays a very important role. However, the entire surface may not have access to metal ions; the latest research shows that not all BET surface areas can participate in ion adsorption. Van der Waals results have revealed that the effectively used are of a porous activated carbon electrode is \(\sim\)55% of the
# Decision Support Model for Allocating Maintenance Budgets for Bridges
## 4 Proposed Model
### Computation Process Workflow
Step 4--Optimization-based simulation  
The overall priority of the structural elements, maintenance costs, and the budget are then used as inputs to the optimization-based simulation in the risk analysis software;  
The probability distribution is defined based on the uncertainty relevant to the two variables (priority and the structural element cost);  
The objective function is set to be the maximum summation of the priorities of the selected structural elements, while the total maintenance cost for the same structural element must be equal to or less than the constraint budget;The risk analysis software then randomly selects a set of structural elements between 1 and N (where N is the number of structural elements to be maintained), along with the estimated maintenance cost and its priority;  
The total cost of the selected maintenance plan is compared with the budget;  
If the selected maintenance plan satisfies the budget, the set of selected structural elements is recorded and prioritized;  
The random selection methodology uses the concept of binary decisions. The structural element is either included or excluded from the maintenance plan. If it is to be included, then the decision given a value of 1, and is multiplied by the overall priority;  
If the decision is not to include it, then the decision is given a value of 0. This is performed randomly and iteratively while the model is assigning a decision (1 or 0) for each structural element. The structural element which receives decision 1 means it is included in the maintenance plan and the zeros are excluded;  
The output consists of list of structural elements to be included in the maintenance plan;  
The model will record each list and its total cost and the sum of priority. The lists having a total cost less than the budgeted cost will be compared with each other to figure out the list with the maximum sum of priorities;  
The near-optimum solution (maintenance plan) out of these solutions (maintenance plans) is the one with the highest priority and the least cost, which also satisfies the budget. Termination conditions  
The termination condition in the optimization-based simulation is limited to the number of iterations. The model reports the set of structural elements (maintenance plan) with the maximum sum of priority, while the sum of the maintenance cost is equal to or less than the budget. Figure 4 illustrates the model, including the major processes and data input. Figure 4: Overview of the model.
# Diffusion Model as a Base for Cold Item Recommendation
###### Abstract  
Cold items are a critical problem in the recommendation domain because newly introduced items lack user-item interactions to train accurate collaborative filters (CFs). Recent studies have adopted neural networks such as MLPs and autoencoders to predict collaborative embeddings learned by CFs, using items' side information available at the time of registration. As a generative model, diffusion models have achieved success in various fields, such as image generation and natural language processing, through their superior generative capability. This paper proposes a diffusion model as a base for cold item recommendation by generating collaborative embeddings for cold items. First, using a diffusion model with our customized predictor, we directly generate items' collaborative embeddings conditioned on their side information. Then, a second-stage refiner, which adopts simple MLPs and dropout, is trained to calculate the final recommendation scores. The proposed method requires only the user embeddings from the existing model and the side information of cold items, making it easy to integrate into existing recommender systems. Extensive experiments on real-world datasets show that the proposed method outperforms state-of-the-art baseline methods and indicates the potential of diffusion models in cold item recommendation.  
c 2025 15 4784 +
Footnote †: journal: Applied Sciences
# Study on Active Tracking of Underwater Acoustic Target Based on Deep Convolution Neural Network
## 2 Kalman Filter Active Tracking Based on Deep Convolutional Neural Network  
After the active sonar emits sound waves, the echo signals received continuously during the period include various reverberation and sound wave scattering caused by impurities. The difference in target geometry will also cause the difference in the echo signal. We can use the information carried in the target echo signal to track the target. Active sonar tracking a moving target is a process of predicting the location, searching for the target, judging the target, and associating the data. The schematic diagram of the DCNN-KFAT process proposed in this paper is shown in Figure 1.  
We use periodic pulse signals as active sonar transmission signals, and receive echo signals with a uniform linear array. In the preprocessing step, the target is detected by beamforming and matching filtering the received echo signal, and the time-frequency spectrogram of the target is obtained after azimuth-weighting the signal according to the detected target azimuth; we label the different targets and generate the dataset, and DCNN is used to train the dataset.  
In the subsequent tracking step, the echo signals are weighted by different azimuths to obtain a time-frequency spectrum image of the signal to be detected, and the trained model is used for identification to obtain the azimuth and distance of the target. All steps are described in detail in the rest of this section.
# An Improved YOLOX Model and Domain Transfer Strategy for Nighttime Pedestrian and Vehicle Detection
## 5 Experimental Comparison and Discussion
### Analysis
#### 5.3.2 Ablation Studies  
The improved YOLOX algorithm proposed in this paper mainly includes five improvements, which were model structure reparameterization and light weighting, the CA attention mechanism, feature pyramid improvement, the confidence loss function based on Varifocal Loss, and the CIOU-based bounding-box regression loss function. In order to verify the effectiveness of each improvement point of the algorithm in this paper, an exhaustive experimental study of model ablation is conducted on the nighttime vehicle/pedestrian dataset, and the results are shown in Table 2.  
Among them, Baseline represents the original YOLOX algorithm trained with default parameters, and its mAP result is 76.5%; after the model structure reparameterization and light weighting, the model weight size was reduced by approximately 4 MB, and the accuracy is almost unchanged, and the mAP at this time is 76.6%; after the CSP components at positions P3, P4, and P5 in the YOLOX backbone network were added with the addition of the three-layer CA attention module, the accuracy increased by 1% with almost the same model size, and the mAP is 77.5%, but the inference time increases due to the addition of too many layers. The inference time also increased because the model structure became  
Figure 6: (**a**) Comparison of mAP for different algorithms. (**b**) Comparison of PR curve for different algorithms.  
more complex; then, using Varifocal Loss as the confidence loss, the model inference time was almost unchanged, but the accuracy improved by 0.4%, and the mAP was 79.7% at this time; finally, using CIoU loss as the bounding-box regression loss, the accuracy improved by another 0.4%, and the mAP was 80.1% at this time.
# A Compact Low-Power LoRa IoT Sensor Node with Extended Dynamic Range for Channel Measurements
## 2 Hardware Design
### RF-Section
This is rewritten to find the received power at the antenna:  
\[\text{P}_{\text{ANT,dBm}}=\text{P}_{\text{RX,dBm}}+\text{ATT}_{\text{dB}}. \tag{3}\]  
Attenuation levels can vary between 0 dB and 31.5 dB and can be changed in 0.5 dB steps using an SPI bus. The pertinent attenuation adjustment algorithm is visualized in the flowchart depicted in Figure 4. In short, this algorithm either increases the attenuation by 1 dB when the SNRs of the received packets approach the saturation level of the detector--which is approximately equal to 5 dB, so a threshold of 3 dB is chosen--or lowers the attenuation by 1 dB when three subsequent packets are no longer received. The amount of lost packets needed to cause a decrease of attenuation was  
Figure 3: SNR measurement calibration for 434 MHz (**left**) and 868 MHz (**right**). chosen at three because a lower value may cause the system to react to sudden changes in SNR caused by momentaneous interference issues while a higher value may cause the system to lose too much packets before adapting to a real change in propagation conditions. The step size was chosen at 1 dB because using the full 0.5 dB resolution of the attenuators would cause the amount of time needed for the algorithm to settle to the appropriate settings to be too high given that the amount of packets that can be sent per minute is subject to quite severe duty cycle regulations. On the other hand, using an even larger step size would result in a more sizable loss of resolution, which is also undesirable. Given that the LoRa module has a linear SNR measurement range corresponding to a signal level range of 32 dB, a simple calculation reveals that the use of attenuators at the receiver's side of the link increases the node's theoretical dynamic range for signal measurements to 63.5 dB. Furthermore, by using the attenuators of the transmitter as well, this dynamic range can be shifted over an additional 31.5 dB. However, since this affects the transmitted signal power, receivers located further away from the transmitter could experience an increase in packet loss when this strategy is used. Consequently, care should be taken when shifting the dynamic range when multiple receivers are used. In addition to received power measurements, more straightforward link statistics such as packet loss can be gathered by using the general purpose microcontroller described earlier.
## 2 Methods  
### SHPB System  
The SHPB system, originally developed by [PERSON] to obtain the dynamic stress-strain relationship of several materials [18], has been taken as the most successful loading technique for investigating the dynamic characteristics of rock [19]. After more than a decade's development and modification by geotechnical researchers, SHPB system now can be used for coupled static and dynamic loading test considering axial stress and confining stress. [PERSON] et al. [20] modified the conventional SHPB apparatus by adding a thin copper disk to the impact surface of the incident bar to obtain compressive stress-strain data for rock materials. [PERSON] et al. [9] developed a new testing system for coupling static and dynamic load experiments on rock based on a previous SHPB system. [PERSON] et al. [21] proposed a new technique based on an SHPB system to achieve triaxial compression loading of materials. The detailed introduction to SHPB history as well as its modification and application have been comprehensively reviewed in literatures [22]. Furthermore, the pulse-shaping techniques and stress-strain equilibrium of SHPB tests have additionally been thoroughly studied by many researchers [23; 24]. In this paper, the SHPB system was adopted to apply coupled static and dynamic loads on a coal sample in order to investigate the stress-strain relationship, failure mode and energy evolution of coal subjected to coupled static and dynamic loads.  
The principle of the SHPB system for coupling static and dynamic loads experiment is briefly described in this section. As shown in Figure 1, the numerical modeling is based on the experimental coupled static and dynamic SHPB system developed by [PERSON] et al. [9]. It involves a striker bar, an incident bar and a transmission bar according to the standards proposed by the International Society for Rock Mechanics (ISRM) [25]. In addition, a pre-loading unit was mounted to apply static pre-stress before the impact test. The specimen was sandwiched between the incident and transmission bar. First, a given axial pre-stress will be applied to the specimen by the pressure loading unit at the end of the transmission bar. The strike bar will then be released to impact the incident bar once the pre-stress condition is stable within the SHPB system and specimen.  
The strain rate \(\dot{\varepsilon}(t)\), strain \(\varepsilon(t)\) and stress \(\sigma(t)\) of sample can be expressed according to one-dimensional stress wave theory [26]:  
\[\dot{\varepsilon}(t)=-2\frac{C}{L_{S}}\varepsilon_{R}(t) \tag{1}\]  
\[\varepsilon(t)=-2\frac{C}{L_{S}}\int_{0}^{t}\varepsilon_{R}(t)dt \tag{2}\]  
\[\sigma(t)=\frac{EA}{A_{s}}\varepsilon_{T}(t) \tag{3}\]  
where \(C\) is the elastic wave speed in the bars, \(L_{S}\) and \(A_{s}\) are the cross-sectional area and the length of the sample, and \(E\) is the Young's modulus of bars. Detailed explanation and derivation procedures of these three equations have been given by [PERSON] et al. [27].
## 3 Results and Discussion
### Physico-Chemical Characterization of the Hydrolysates
#### 3.1.6 Total Phenolic, Flavonoids, and Anthocyanations Content  
Table 3 presents the contents of total phenolic, flavonoids, and anthocyanins in the different protein hydrolysates. Generally, the contents of these compounds significantly increased in a concentration-dependent manner. The BKH (800 \(\mu\)g/mL) recorded higher values of phenolic, flavonoids, and anthocyanins amounting to 63.3 mg GAE/g, 16.2 mg QE/g, and 0.6 mg/mL (800 \(\mu\)g/mL), respectively. The levels of phenolic and flavonoids were not significantly different among the three protein hydrolysates (BKH, RKH and WKH) but the levels of anthocyanin in RKH and WKH were significantly (\(p\leq 0.05\)) lower than in BKH, recording 91, and 45 % reductions, respectively. [PERSON], et al. [77] found that the polyphenols contents in dark red bean protein hydrolysate were 44.3 mg GAE/g, and 38.89 mg GAE/g [PERSON], et al. [43]. The physicochemical properties of protein may affected by protein-phenolic interaction; the peptide activity may increase by blocking some amino acid side chains, thereby increasing the bioavailability and activity of polyphenols [78]. Polyphenols become entrapped in the peptide fragments and the enzyme hydrolyzes the protein, and becomes bound with polyphenols. Therefore, the polyphenols will be released and their free content will increase [79]. Additionally, it was found that alcalase hydrolysis of rice bran protein for 10 min was enough to extract all the bound phenolic acids [80].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Protein**} & \multicolumn{6}{c}{**Functional Properties**} \\ \cline{2-6}  & **HT** & **WAC** & **OAC** & \multicolumn{2}{c}{**FS (min)**} \\ \cline{3-6}  & **(min)** & **(g/g)** & **(g/g)** & **pH 3** & **pH 5** & **pH 7** \\ \hline \multirow{3}{*}{BKH} & 0 & 6.9 \(\pm\) 0.5 d  & 6.8 \(\pm\) 0.2 d  & 23 \(\pm\) 0.1 c  & 65 \(\pm\) 0.3 c  & 26 \(\pm\) 0.6 c  \\  & 30 & 9.4 \(\pm\) 0.2 a  & 10.5 \(\pm\) 0.6 a  & 27 \(\pm\) 0.3 a  & 88 \(\pm\) 0.9 a  & 30 \(\pm\) 0.8 a  \\  & 0 & 6.7 \(\pm\) 0.4 d  & 6.5 \(\pm\) 0.3 d  & 20 \(\pm\) 0.8 d  & 44 \(\pm\) 0.8 e  & 23 \(\pm\) 0.2 d  \\  & 30 & 8.2 \(\pm\) 0.8 b  & 9.3 \(\pm\) 0.1 b [FONOTE:b]Footnote d: footnotemark: [ENDFOOTNOTE] & 25 \(\pm\) 0.6 b  & 75 \(\pm\) 0.7 b  & 28 \(\pm\) 0.5 b  \\  & 0 & 6.1 \(\pm\) 0.3 e  & 6.0 \(\pm\) 0.1 e  & 18 \(\pm\) 0.9 e  & 29 \(\pm\) 0.1 f  & 21 \(\pm\) 0.4 e  \\  & 30 & 7.2 \(\pm\) 0.1 c  & 8.1 \(\pm\) 0.2 c  & 22 \(\pm\) 0.7 c  & 50 \(\pm\) 0.9 d  & 25 \(\pm\) 0.3 c [FONOTE:d]Footnote d: footnotemark: [ENDFOOTNOTE] \\ \hline \hline \end{tabular}  
* HT, hydrolysis time; WAC, water-absorbing capacity; OAC, oil-absorbing capacity; FS, foaming stability. All values are expresses as means \(\pm\) SD. Means with different lowercase letters in the same column indicate significant differences at \(p\leq 0.05\) by LSD.  
\end{table}
Table 2: Functional properties of black, red and white kidney bean protein hydrolysate (BKH, RKH, and WKH) obtained after 30 min Alcalase hydrolysis at 37 \({}^{\circ}\)C.