esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Technologies as Common Practice in Industrial Maintenance: What Do Experts Say?
## 4 Results
### Answering the Research Questions
#### 4.2.1 Human  
* **Physical safety for the working persons:** In intelligent manufacturing systems, hazards to the physical health of the machine operator can arise both during regular operation and in the event of a malfunction. When implementing a remote maintenance strategy, these hazards must be reassessed. It must be assessed to what extent they change through the implementation of remote processes and whether the existing personal safety measures such as training and personal protective equipment are still appropriate.
* **Qualification of the machine operator:** Intelligent manufacturing systems are usually complex in structure, functionality and operation. The implementation of a remote maintenance strategy can change the requirements for the qualification of both the machine operator and the maintenance staff. It must be examined how the activities of the machine operator and the maintenance expert change, among other things with regard to the work task as well as the use of technical aids, and whether a qualification gap arises as a result.
## 3 Adaptive Beamwidth Control  
Several studies have been conducted on adapting to control the beam divergence angle, for example, using a component of an FSO terminal with a basic design that uses a common optical path and an established adaptive beam control [30]. Furthermore, a method of adaptively adjusting the beam divergence angle using a variable focal lens has  
Figure 3: Scintillation index for pointing error when two collimated beam sizes, 10 and 5 cm, are set for a link distance of 20 km.  
been studied [18; 19; 20]. In this study, we added a beam divergence controller to the existing method to control the beam divergence angle adaptively.  
Figure 4 shows the proposed adaptive beam control flow. FSO and radio frequency (RF) links are assumed to be utilized concurrently in the OGS and UAV. First, the receiver starts coarse pointing based on RF Global Positioning System (GPS) information. Then, the beacon controls the gimbal according to the information generated in the focal plane array (FPA). When the beacon is aligned with the FPA, a narrow communication beam is sent to start fine-pointing. When the communication beam gets aligned, a fast-steering mirror (FSM) starts fine-tuning using the information in the quadrant photodiode (QPD). When data transmission begins, the communication beam is divided between the photodiode (PD) and QPD, which receive and track the data, respectively. The QPD continuously reads the position of the received beam, calculates the pointing error, and sends it to the transmitter using the RF feedback link. If the QPD does not receive the communication beam, the FPA will check whether the beacon signal is detected, and the gimbal will be controlled for alignment. Once the beacon is aligned, the communication beam is aligned again.  
Figure 5 shows the schematics of the PAT procedures with adaptive beam control for OGS-to-UAV communication. Both the OGS and UAV use a beacon beam, a separate telescope that exchanges a communication beam, and an aperture that exchanges a beacon beam. To prevent interference between uplink and downlink, wavelengths of the communication beam were taken as 1550 and 1570 nm, respectively. On the basis of the GPS information received from RF signals, the gimbal controls the body, which can be oriented in a wide range. Then, the beacon is sent in that direction, and the beacon adjusts the gimbal more finely according to the information arrayed to the FPA. Once the arraying procedure is complete, a communication beam is sent after passing the FSM. It will then be divided into a PD and a QPD by beam splitter (BS). The PD receives data and controls the FSM using the information in the QPD so that the beam can be accurately oriented. The QPD sensor data are used as a beam spot monitor, and the pointing error can be used to calculate the position of the beam on the QPD. The estimated pointing error is exchanged using the RF feedback link. Finally, the beam divergence controller controls the transmit beam angle according to the estimated pointing error.  
Figure 4: Adaptive beam control flowchart.
## 2 Materials and Methods
### Proposed Improved MDDS Method
#### 2.2.2 Proposed Phase Error Compensation and Pre-Distortion  
In this study, we propose an additional block diagram to compensate for the phase error caused by MDDS generation. The additional block diagram, referred to as the compensation phase error (CPE), is depicted in Figure 3 and calculated as follows:  
\[\phi(t)=(\alpha/2)t^{2}+\beta t+\gamma, \tag{7}\]  
\[\phi_{error}=\phi_{output}-\phi_{dial}, \tag{8}\]  
Figure 2: Block diagram for multi-DDS (MDDS). Each output is merged into a multiplexer (MUX).  
where \(\phi(t)\) is the phase value varying time and \(\alpha\), \(\beta\), and \(\gamma\) are chirp rate, initial frequency, and initial phase, respectively. \(\gamma\) will be changed by calculating the phase error. \(\phi_{error}\) is the phase error, \(\phi_{output}\) is the output of Simulink, and \(\phi_{ideal}\) is the calculated phase value [22]. We simulated the MDDS and compared the output with the ideal value, which was derived from a formula. The difference between the two values, the phase error, was input to the CPE block. The ideal output was obtained by compensating for the phase center. The practical hardware output was different from the simulated ideal output due to the hardware characteristics distortion and circuit loss. A pre-distortion method was also applied to Simulink because the developed waveform generator could not be modified.  
Figure 4 illustrates the pre-distortion process to compensate for the phase error varying time in the X-band frequency spectrum. First, we measured the phase error varying time in the X-band spectrum. We then selected the pre-distortion section, which has a relatively large phase error. The number of sections and the section lengths were determined by considering compensation gain and hardware efficiency.
# Weighted Matrix Factorization Recommendation Model Incorporating Social Trust
## 5 Experimental Evaluation
### Impact of Parameters
Therefore, for Epinions, a higher trust weight is required to achieve the best recommendation accuracy compared to Ciao. As can be seen in Figure 2, the three parts of data after integration include the predicted ratings obtained solely from trust relationships, those obtained solely from similarity relationships, and the ratings derived from the integration of the overlapping parts of these two types of predicted ratings. The respective weights of these three parts can be determined by Equation (12), with weight \(w_{3}\) being the comprehensive weight after integration. As shown in the figure above, the accuracy of the recommendation model that considers only trust relationships is superior to the accuracy of the recommendation model that considers only similarity relationships. Therefore, in the three parts of the data, trust relationships should have a greater weight. The model fixes the ratio of trust weight \(w_{1}\) to similarity weight \(w_{2}\) at 2:1, thereby deriving different weights for \(w_{3}\). As shown in Figure 4, the integration of trust and similarity relationships further enhances the accuracy of the algorithm's recommendations. Figure 5 shows the impact of weight \(w_{3}\) on the fit of the model for different datasets, Epinions and Ciao. The figure indicates that for both the Epinions and Ciao datasets, the model achieves optimal fit when the weight \(w_{3}\) is 0.1. As the weight increases, the fit of the model gradually decreases. However, as shown in Figure 4, on the Epinions and Ciao datasets, the highest recommendation accuracy is achieved when the weights \(w_{3}\) are 0.2 and 0.15, respectively. This suggests that in this model, fit does not always correlate positively with recommendation accuracy. Although increasing the weight \(w_{3}\) reduces the fit, it may simultaneously increase the amount of effective information extracted by the model. Figure 4: Impact of w1, w2, and w3 weights on model accuracy on Epinions and Ciao.
# Scheduling of Industrial Control Traffic for Dynamic RAN Slicing with Distributed Massive MIMO +
## 7 Results
### Discussion  
A clear direction for future work following these results is to widen the range of parameters tested, in particular, the maximum frame length, but also the maximum number of pilots and the traffic profiles. Our focus in this work has been to test the feasibility of solving the different formulations and compare them, and since the solution times for FRM/2 were very long, this limited the parameters that could be included in such a comparison.  
However, we now see a clear result in terms of the formulations and solution algorithms that are the best candidates for use in practical deployments. When the frame length is fixed and known in advance, FRM/1 is suitable for use and very efficient to solve. When the frame length is not known and should be solved, the iterative approach using FRM/1 works much better than directly using the general formulation FRM/2. Given this, in future work, we can focus on these feasible methods and test larger problem instances and longer frame lengths.  
The frame length is, however, also limited by practical considerations. First of all, it may be desirable to fit one frame within a TTI. For 5G systems, the TTI is variable depending on the type of traffic served, but the TTI chosen may still impose a constraint on usable frame lengths. The frame length may also be limited by considerations, such as error handling and schedule changeover, as both of these functions are likely to be placed at a frame boundary. The higher the granularity desired, the shorter the frame needed.  
Similarly, the maximum number of pilots to be used in any slot is likely to be affected by external considerations and cannot be freely set so as to benefit the optimization problems we have presented here. In practice, in 5G massive MIMO systems, the total number of  
Figure 8: Pilot rate minimization with fixed frame length: optimal frame length.  
pilots available for all traffic is determined by such factors as the total channel bandwidth and subcarrier spacing, the channel coherence time and frequency, and the pilot signal scheme used. This then must be divided amongst the different network slices--even though the industrial control slice has high priority, some minimum allocation may be needed for other slices, reducing the maximum number of pilots available.  
It is thus not straightforward to set these parameters in a general way in a study such as this one. However, in a practical deployment, the factors and constraints discussed above would be known and can thus be used to configure the optimization problems appropriately. Then, our formulations provide a means to schedule both deadline-based, periodic traffic, and rate-based background traffic together in an efficient way that guarantees quality of service requirements for demanding industrial control traffic are met, while also maximizing the radio resources available to be used opportunistically by other slices in a dynamic RAN slicing context.
## 3 Results and Discussion
### Comparison of In Vitro vs. In Vivo Digestion Models  
Based on our findings, the immobilized cell cultures exhibited high viability levels (>6 log CFU/g) following both in vitro and in vivo digestion. However, it is important to emphasize that, while a prominent protective effect of cell immobilization was observed in the simulated in vitro digestion model compared to free cells, this level of effectiveness was not present in the in vivo model. Specifically, after in vitro digestion, freeze-dried free _L. rhamn_s OXLAL-1 cells demonstrated a survival rate of 33.42%, resulting in a final cell concentration of 3.04 log CFU/mL, which was substantially lower than the viability of the corresponding free cultures following in vivo digestion (>6 log CFU/g of mice feces). Despite this discrepancy, cell immobilization exerted a strong influence on the rise of lactobacilli levels in the intestinal fluids and feces compared to free cells, a result indicating an improvement in cell viability.  
In general, in vitro methods have long been favored in the field of probiotics research due to their simplicity, cost-effectiveness and capacity to evaluate multiple microbial strains concurrently. However, the ability of presumptive probiotic strains to survive under the challenging conditions of the GI tract has, for the most part, been studied by outdated in vitro tests that inadequately replicate the successive stress conditions that occur in the human body [15]. For instance, conventional in vitro static models employ constant pH levels in simulated gastric fluids, whereas, in reality, the pH levels in the stomach can fluctuate between 1-2 and rise to 4-5 following food consumption. In the same manner, in vitro tests assessing bacterial cell tolerance to bile salts often fall short in simulating the dynamic levels of bile concentration found in an in vivo model. Moreover, most tests commonly utilize porcine or bovine bile, which may not have the same impact on cell viability as human bile [49].  
In our study, we sought to address the limitations of traditional in vitro digestion models by employing a more complex approach based on the highly cited study by [PERSON] et al. [20]. By doing so, we aimed to gain a more comprehensive understanding of the variations observed in the survival rates of free and immobilized cell cultures among the two digestion models applied. Finally, even though discrepancies between the two models were observed, these results align with the existing literature that describes the challenges in assessing the survival of probiotic bacteria in vitro and the importance of in vivo validation.
# Revealing Development Trends and Key 5G Photonic Technologies Using Patent Analysis
## 2 Literature Review
### Patent Technology Network Analysis  
In recent years, studies have explored the development path of specific technologies using network analysis [24; 25; 26], through which the knowledge map and technology positioning of a country can be determined [27; 28]. Moreover, the current state of technology transfer and technology industrialization has been explored through patent analysis [29; 30; 31]. Network analysis can accurately display the evolution pattern of technologies and communication paths of information. The analysis of patent data can provide objective and feasible information, including the number of patents, year of patent approval, and technology type [29]. Accordingly, analyzing the development of specific technologies through patent data is valuable. Therefore, this study explored the connectivity and co-occurrence between technology nodes using network analysis. The division of technology nodes was based on the studies of [PERSON] and [PERSON] [32] and [PERSON] and [PERSON] [33]. Mature patent classification structures were used as the foundation of our analysis and the key players in the application of photonic technology in 5G technology were analyzed through patent technology network analysis to observe the areas of focus of 5G photonic technology and to understand technology development trends.
# Denoising Diffusion Models on Model-Based Latent Space
## 2 Related Works
### Latent Diffusion Models  
As briefly stated in the introductory section, Stable Diffusion [6] achieved impressive results in image generation with DDPMs by shifting the generation process in latent space. This choice is motivated by the inherent difficulty of training DDPMs for high-resolution image generation, due to the waste of expressive capacity for generating irrelevant image details and the very high computational cost. The shift to latent space introduces a strong GreenAI-boosting perceptual and spatial compression, simplifying training and reducing computational cost. The latent space is defined by an encoder \(\mathcal{E}(x)=z\) which maps an image \(x\in\mathbb{R}^{w\times h\times c}\) into a latent representation \(z\in\mathbb{R}^{\frac{\pi}{\pi}\times\frac{\pi}{\pi}\times c}\) and a decoder \(\mathcal{D}(z)=\overline{x}\) which reconstructs \(\overline{x}\approx x\) in the image space. The pair \((\mathcal{E},\mathcal{D})\) defines a Variational Autoencoder (VAE) pretrained on the Open Images dataset [11] (and frozen when training the diffusion model), trained by simultaneously minimizing a reconstruction term \(||x-\overline{x}||_{2}^{2}\) and a regularization term useful to bound the variance of the latent space. Good results have been achieved with VQ-VAE regularization [12], which introduces a Vector Quantization (VQ) layer to regularize the latent space. This, however, is not to be confused with the VQ-based compression method introduced in Section 3.1, especially since \(z\) is extracted from the VAE bottleneck before the VQ layer, which is hence embedded in the decoder.  
Defining the generative process in latent space means redefining the loss in Equation (4) as a function of \(z\):  
\[\mathcal{L}_{L}(\theta)=||\epsilon-\epsilon_{\theta}(z_{t},t)||_{2}^{2} \tag{6}\]  
Hence, the reverse diffusion model is used to sample \(\bar{z}\), and at the end of the generation process, the sampled image \(\bar{x}=\mathcal{D}(\bar{z})\) is recovered using \(\mathcal{D}\). The work presented in this manuscript is conceived as an ablation of this method by substituting the deep latent space defined by the VAE with a shallow latent space defined by a model-based lossy image compression technique.
###### Abstract  
The use of 222 nm far-UVC radiation can be an effective means of disinfecting public buses against viruses, including SARS-CoV-2. However, it can cause degradation of the mechanical and visual properties of interior materials. The purpose of this study is to investigate the effects of 222 nm far-UVC radiation on the color and mechanical degradation of materials used to construct public bus interiors. This research work involves exposure of samples of materials commonly used in bus interiors to various levels of far-UVC radiation and measuring and evaluating changes in color and mechanical properties. The results of the study showed that far-UVC irradiation causes significant color degradation (\(\Delta E00>\)5) in all the polymeric materials tested, after 290 J/cm\({}^{2}\) radiant exposure. In addition, significant changes in mechanical properties were observed when evaluating elasticity modulus, elongation at ultimate strength, elongation at break, and tensile strength. A particularly large decrease in elongation at break (up to 26%) was observed in fiber-reinforced composite materials. The results of this study can be used as a guide for the development of protocols for the use of far-UVC disinfection in public transportation, which can help limit the transmission of infections while preserving the integrity and visual properties of bus interior materials.  
far-UVC radiation; COVID-19; color difference; mechanical degradation; public bus Article
## 4 Simulation Results  
This section evaluates the performance of the proposed adaptive NPID controller (hereafter referred to as ANPID) in terms of setpoint tracking, disturbance rejection, and robustness to parameter variations. The performance of the ANPID is assessed using the model defined in (4) and MATLAB 2018a software, and compared to that of an adaptive PID controller (hereafter referred to as APID). The weights, centers, and widths of the RBFNN for both controllers are subsequently updated based on the algorithms derived in the preceding section for a fair comparison. Setpoint tracking performance is evaluated based on overshoot (\(M_{p}\)), rise time (\(t_{r}\)), 5% settling time (\(t_{s}\)), and the integral of absolute error (\(IAE\)), which is assessed as follows:  
\[IAE=\int_{0}^{\infty}\lvert e(t)\rvert dt \tag{41}\]  
For disturbance rejection, parameter variations, the perturbation peak (\(M_{peak}\)), recovery time (\(t_{rcy}\)), and \(IAE\), \(M_{peak}\) denotes the maximum absolute deviation from the setpoint, while \(t_{rcy}\) signifies the time required to return within 2% of the setpoint following a disturbance.  
Figure 8: Proposed adaptive NPID control scheme based on RBFNN.
## 4 D-Leach
### Steady-State Phase  
When the setup phase is completed, data transmission begin. Figure 11 shows the data transfer process of the sensor node.  
D-LEACH distinguishes the CH according to the distance from the BS. The CH located close to the BS is the upper CH, whereas the CH farther from the BS is the lower CH. The algorithm uses direct transmission, single-hop transmission, and multi-hop transmission utilizing relative distances. The member nodes of the CH transmit data to the CH. The sensor nodes, not the member nodes of the CH, transmit directly to the BS. The CH transmits data to the nearest upper CH that is closer to the BS than itself and a shorter distance than the direct transmission distance to the BS. If there is no upper CH corresponding to this condition, the CH transmits directly. Figure 12 shows the steady-state of D-LEACH.  
Figure 11: Data transmission process of the sensor node.
## 5 A Case Study on Reliable Pipeline Monitoring Using Cyber Physical Sensor Networks  
Oil and gas pipeline monitoring provides a novel example where WSN can provide remote monitoring while CPS integration can be leveraged to apply real time information and analytics of the underlying framework [32]. With a number of possible pipeline deployment techniques available, all efforts unswervingly reflect the characteristics of the medium needed for transportation that depends upon the environmental, strategic and economic conditions. For intelligently providing flexible and reliable CPSN-based monitoring, factors like sensor layout, data transmission methods, sensor node power concerns, data processing, analysis and inference points, operational design and framework in addition to network topology, infrastructure and sensing related technologies are the focus of this section.  
The oil and gas distribution pipeline deployment methods can be widely characterized into underwater, above ground and buried pipelines. Since leakages could be detrimental to the surroundings, integral pipeline monitoring is essential and needs to be reliable and in real time. The main sensing techniques can be categorized into reactive ones that detect the presence of leaks only once they occur while the proactive methods monitor the condition of pipelines gradually over time to prevent leakage occurrence [33].
## 4 Results
### Comparison of LDS U-Net with Other Contemporary Models
#### 4.2.3 Evaluation of the Number of Bony Features Identified  
Identification of distinct bony features is a key criterion for evaluating the performance of various segmentation models. In a truth mask of a spine ultrasound, depending on the scanning coverage area, there are nine pairs of TBFs, a T12 region (one pair of bones), and six LBFs, making a total of 26 bony features. The total number of distinguishable bony features in the outputs of each model is manually recorded. In many cases, the bony features are either conjoined or fragmented and are not recorded as meaningful features. Table 3 shows that LDS U-Net provides a larger number of meaningful bony features when compared to other models. The proposed LDS U-Net could provide the highest percentage of segmented images with all 26 features fully detected (76.59%).  
Figure 13: Four spine image sets (**a–d**) consisting of the input image, truth mask, and segmentation results using U-Net, Attention U-Net, MultiResUNet, and the proposed LDS U-Net model.