esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation
### Simulation Study
#### 5.3.2 Impact of the Number of Nodes  
Figure 9a compares the performance on the average latency between OSCAR and Orchestra by varying the number of nodes from 40 to 100. From this figure, we can clearly see that OSCAR outperforms Orchestra on average network latency. Thus, the greater the number of nodes in the network, the more the difference is apparent, corroborating the prior experimental results. OSCAR improves communication delay, and this observation can be justified by the fact that the most congested nodes are assigned more slots to evacuate the traffic. Indeed, the particularity of OSCAR is that the system grants more transmission opportunities to the nodes which need them most, namely, the nodes close to the root in the case of convergecast traffic.  
Figure 9b shows the average duty cycle performance (and thus energy consumption) between OSCAR and Orchestra. We see that between 40 and 60 nodes the average duty cycle of the network is in favor of OSCAR. This can be explained because some nodes are less active than others and benefit from the reduction mechanism of slots allocated to inactive nodes introduced by OSCAR. On the other hand, the good performance of OSCAR, in terms of latency reduction, results in higher power consumption when the number of nodes reaches 80 and the traffic load becomes heavier. Finally, Figure 9c shows the average packet delivery ratio of Orchestra and OSCAR. As with the previous simulations, 2000 packets were sent by each node to the root. For a network of 60 nodes or less, the two systems have similar packet delivery performance, which shows that OSCAR maintains Orchestra's good performance in terms of packet delivery. Howe  
Figure 9: Comparison between Orchestra and OSCAR according to the number of nodes in the simulation: (**a**) average end-to-end delay, (**b**) average radio duty cycle, and (**c**) average packet delivery ratio.  
nodes is greater than 60, the gap becomes more pronounced and better results are observed for OSCAR compared to Orchestra. This observation can be explained by the fact that as the number of nodes increases, the traffic load also increases, causing packet loss at congested nodes. However, as seen previously, OSCAR minimizes this congestion by allocating more slots to these nodes, which results in a better delivery ratio.  
Fundamentally, OSCAR shows that in larger networks, it can also bring significant latency and packet delivery advantages that widen as the network size is increased. OSCAR exhibits higher energy consumption in networks with significant amount of convergecast traffic (e.g., 80 nodes). However, the more drastic improvements in latency and packet delivery enabled by OSCAR as the number of nodes is increased offset the increased energy consumption.
# A Low-Complexity Volterra Filtered-Error LMS Algorithm with a Kronecker Product Decomposition
## 5 Performance Evaluation  
This section evaluates the proposed DVMFELMS algorithm's performance and compares it with the FELMS algorithm, the VFELMS algorithm, the FEGFLNN algorithm, and the FEEMFNL algorithm. We evaluate the algorithms in three different nonlinear situations; the primary path has a nonlinear process in the first case, the primary and secondary paths have nonlinearity in the second case, and there is a real measured path with an intensely saturated signal in the last case. The normalized mean square error (NMSE) [11] is used to evaluate the performance of the algorithm, which is defined as  
\[NMSE=10\log_{10}\left(\frac{E\big{\{}e^{2}(n)\big{\}}}{\sigma_{d}^{2}}\right), \tag{28}\]  
where \(\sigma_{d}^{2}\) is the mean square energy of the primary noise. All results presented in this section are based on an ensemble average of 100 independent runs for each algorithm. The primary path \(P(z)\) is set to be \(-P(z)\) in the middle of the iteration to study all algorithms' stability and tracking capability. Furthermore, the step-sizes are all selected to achieve the maximum convergence rate with the minimum achievable NMSE.
## 3 Results  
### Fermentation of Fractan-Type Carbohydrates by BB-12 in Monoculture (Experiment 1)  
Potential fermentation of seven fructan-type carbohydrates by BB-12 was investigated in monoculture incubations with focus on endpoints related to microbial metabolic activity and growth of BB-12. During a first series of experiments, in which no sterile fecal suspension was co-administered, BB-12 did not ferment any of the fructans (data not shown). While still no growth was observed in the blank that included sterile fecal suspension (indicating absence of fermentation of the nutrients in the background medium by BB-12), each of the seven fructan-type carbohydrates was fermented by BB-12 (in presence of sterile fecal suspension), as observed by the significant reduction of pH, stimulation of acetate and lactate levels and increased numbers of BB-12 (Figure 2).  
Fermentation by BB-12 differed between the fructan-type carbohydrates based on their degree of polymerization. Strongest metabolic effects were observed upon supplementation of oligofructoses (OF1 and OF2), resulting in final levels of approximately 30 mM acetate and 4.5 mM lactate, mainly by enhanced production during the 6-24 h time interval. In contrast, supplementation of the three long-chain inulins (IN1, IN2 and IN3) resulted in similar, though less pronounced, effects, with an approximate increase of 7 mM acetate and 0.5 mM lactate after 48 h of incubation. Treatment with OF/IN1 and OF/IN2 exerted intermediate effects. Furthermore, propionate, butyrate, bCFA and gas were not produced in the symbiotic incubations (data not shown), which is in line with the inability of BB-12 to produce gas and these metabolites, thus confirming sterility of the faecal suspensions.  
In consistency with aforementioned metabolic data, growth of the BB-12 strain was observed for all fructan-type carbohydrates. Strongest stimulatory effects (approximately factor 150 increase versus start of the incubation) were observed for the oligofructoses (OF1 and OF2), while mildest stimulatory effects were observed with the long-chain inulins (IN1, IN2 and IN3). Finally, OF/IN1 and OF/IN2 resulted in intermediate enrichments.
# Multi-Band Polarization Imaging in a Harsh Sea Fog Environment
## 2 Multi-Band Polarization Imaging Principle
### Polarization Imaging Principle of Liquid Crystal Phase Retarder Method  
LCVR is used for polarization imaging, as seen in Figure 1's imaging-principle diagram. When a beam of monochromatic linearly polarized light is directed vertically into the LCVR, the incoming light is split into two vibrational components, Slow and Fast, with equal phase and amplitude, since the vibration direction of the light vector is at \(45^{\circ}\) to the optical axis.  
These two components can be expressed as  
\[\left|E_{f}\right|=E_{0}\cos\theta=\left|E_{s}\right|=E_{0}\sin\theta=\frac{ \sqrt{2}}{2}E_{0} \tag{12}\]  
where \(E_{0}\) is the amplitude of monochromatic line polarized light, \(E_{f}\) is the amplitude of polarized light vibrating in the fast axis direction, and \(E_{s}\) is the amplitude of polarized light vibrating in the slow axis direction, and from Equation (12), it is known that the two vertical components produce a phase difference of \(90^{\circ}\) when they are emitted from the liquid crystal phase delay.  
(1) When a voltage is provided such that the LCVR phase delay is \(\lambda/4\), the phase difference of \(\pi/2\) between the two quadrature components propagating along the fast axis  
Figure 1: Principle of LCVR polarization imaging.  
and the component propagating along the slow axis following modulation by LCVR may be represented as [12], respectively.  
\[\begin{array}{l}E_{f}=\frac{\sqrt{2}}{2}E_{0}\sin(\tau+\delta)\\ E_{s}=\frac{\sqrt{2}}{2}E_{0}\sin\tau\end{array} \tag{13}\]  
When these two components are combined, the left-hand circularly polarized light is obtained.  
(2) When the applied voltage causes the phase delay of LCVR to be \(3\lambda/4\), at this time, the component located in the slow axis propagates a little faster, and a phase difference of \(3\pi/2\) is generated between the component propagating along the slow axis and the component propagating along the fast axis, which can be expressed as follows, respectively,  
\[\begin{array}{l}E_{f}=\frac{\sqrt{2}}{2}E_{0}\sin\tau\\ E_{s}=\frac{\sqrt{2}}{2}E_{0}\sin(\tau+\delta)\end{array} \tag{14}\]  
When these two components are combined, right circularly polarized light is obtained.  
By analogy, when the driving voltage is applied so that the phase delay of LCVR is \(\lambda/2\) and \(\lambda\), the phase difference between the component propagating along the fast axis and the component propagating along the slow axis will be \(\pi\) and 0. When these two orthogonal components are combined, 0\({}^{\circ}\) linearly polarized light and 90\({}^{\circ}\) linearly polarized light can be obtained.  
For the collection of Stokes parameters, this imaging approach employs a voltage-controlled liquid crystal phase delay to modify the polarization device without mechanical rotation. In applications involving polarization measurement, this imaging technique offers a quick reaction time and excellent precision.
## 4 Case Study
### Input Selection by PACF  
After decomposing the DD sequence in the previous section to obtain the five VMFs, it is necessary to establish ELM prediction models for these five subsequences separately. Prior to this, input variables of ELMs should be determined with the PACF method according to Section 2.3. Figure 10 shows the corresponding PACF results for each VMF, and the best input variables for each VMF are shown in Table 3.  
Take VMF1 as an example to illustrate how to select the input variables through the PACF results in Figure 10. For VMF\({}_{1}\), the PACF value falls at the 95% confidence interval when the lag is 6d, so the input variables corresponding to VMF\({}_{1}\) are the five values between \((t-5)\)d and \((t-1)\)d, and the output variable is the value of \(t\)d. In order to guide the operation of the dam more comprehensively, the proposed model is also analyzed for different prediction periods (\(t\)d, \((t+3)\)d and \((t+6)\)d) in this study. Taking VMF\({}_{1}\) as an example, the specific process of implementation is illustrated in Figure 11.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Series** & **Numbers of Input** & **Input Variables** \\ \hline VMF\({}_{1}\) & 5 & \(x_{t-1}\sim x_{t-5}\) \\ VMF\({}_{2}\) & 5 & \(x_{t-1}\sim x_{t-5}\) \\ VMF\({}_{3}\) & 9 & \(x_{t-1}\sim x_{t-9}\) \\ VMF\({}_{4}\) & 4 & \(x_{t-1}\sim x_{t-4}\) \\ VMF\({}_{5}\) & 6 & \(x_{t-1}\sim x_{t-6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The input variables of each VMF.  
Figure 10: The PACF results of VMFs.
## 6 Performance
We performed experiments using the UTOPIA prototype system in the experimental environment to confirm that the developed USVS system works properly and efficiently. Figure 10 shows how the total execution time changed when the number of frames allocated to each Map task was increased. The total execution (or processing) time means the time period from the time when USVS starts to receive the workload from the given networked video cameras to the time when USVS finishes the processing for the given workload. Two workloads were fed at the same time, so, it means that 72,000 frames were given and the size of the total workload was 2.7 Giga Bytes. Fourteen nodes of the  
Figure 8: Flow chart of the object detection algorithm implemented for performance evaluation experiments. cluster system were used in the experiments. When the number of frames allocated to each map task increased, the total processing time in USYS decreased until it reached 200 frames. After that, the total processing time began to increase. Thus we know that there was the optimal number of allocated frames per map task that gives the best total processing time and the optimal number of allocated frames per map task was 200 in the experiments. We think that the optimal number of allocated frames per map task will be different if we change the experiment environments such as the system configuration and the workload. We also performed experiments using the UTOPIA prototype system in the experimental environment to check the scalability of the developed USVS. Figure 11 shows the total processing time in USYS when the number of workloads was increased. In the experiments, the number of frames allocated to each map task was fixed to 200, which was the optimal number of frames per map task, 14 nodes of the cluster system were used, and we increased the number of workloads by 2. The Figure 11 shows that the \"total processing time\" increased linearly. Thus we confirm that the scalability of our USVS was good. Figure 10: The total processing time when the number of frames allocated to each map task was increased. Figure 9: Configuration of the cluster system for Cloud computing in performance evaluation experiments.
## 3 The Results of SBR Latex Mortar Strength Tests
### Brief Conclusion  
The most effective method to reduce floor impact noise is to block the noise transfer at the point of impact. Therefore, the material properties of the mortar that first encounters the impact source should be modified. There are two methods to alter the mortar properties to achieve this:  
1. Enhancing Mortar Strength: Increasing the strength of the mortar compared to general mortar to block the impact transfer.
2. Reducing Mortar Strength: Decreasing the strength of the mortar to absorb the impact.  
As for the methods to change the strength for modifying the properties of mortar, there are two approaches: enhancing the mortar strength to block impact transfer compared to general mortar and reducing the mortar strength to absorb the impact [21]. Accordingly, in this test, the optimal mixture ratio was analyzed by reviewing the test values for compressive and tensile strength as well as the economic validity.  
Through the compressive strength tests of the mortar mixed with Tg 4 \({}^{\circ}\)C SBR latex, it was found that only the mortar mixed with 7% SBR latex showed higher strength compared to the non-mixed mortar. In terms of tensile strength, all mortars mixed with SBR latex exhibited higher strength than the non-mixed mortar. Accordingly, the mixture ratio of 7% SBR latex was selected as the optimal mixture ratio because it surpassed the flexibility problem and provided higher compressive and tensile strength.  
On the contrary, in order to reduce the strength, this test tried to identify the optimal mixture ratio through the tests of the compressive and tensile strength of the mortar mixed with Tg \(-\)16 \({}^{\circ}\)C SBR latex. According to the test results, it was found that, starting with a 5% mixture of Tg \(-\)16 \({}^{\circ}\)C SBR latex, the higher the mixture ratio, the higher the compressive and tensile strength.  
As seen in the study method and process, this study aimed to both increase and reduce the strength of mortar compared to general mortar. Therefore, the mixture ratio of Tg 4 \({}^{\circ}\)C SBR latex at 7% was selected to increase the strength, while the mixture ratio of Tg \(-\)16 \({}^{\circ}\)C SBR latex at 5% was chosen to reduce the strength. These findings provide valuable insights for selecting appropriate SBR latex formulations and mixture ratios to tailor the mortar properties for specific construction needs.
## 4 Materials and Methods
### Quality Analysis
#### 4.3.7 Determination of Sulfate Content  
The sulfate content of the seaweed-based gelling agent used in drinking jelly was determined using a modified method of [PERSON] et al. (2008). An amount of 100 g of drinking jelly was thoroughly dried in an oven to a constant weight to remove all moisture. From the dried sample, approximately 1 g was weighed for analysis and hydrolyzed by boiling with 50 mL of 1 N hydrochloric acid (HCl) for 30 min to release sulfate ions from the sample matrix. After hydrolysis, 10 mL of 0.25 M barium chloride (BaCl\({}_{2}\)) solution was added to the hot solution to precipitate the sulfate as barium sulfate (BaSO\({}_{4}\)). The mixture was allowed to cool to room temperature over 5 h to ensure complete precipitation. The BaSO\({}_{4}\) precipitate was collected on ashless filter paper, thoroughly rinsed with distilled water to remove impurities, and dried. The filter paper containing the precipitate was incinerated in a muffle furnace at 700 \({}^{\circ}\)C for 1 h to produce pure barium sulfate as white ash. After cooling, the ash residue was weighed (W\({}_{2}\), in grams). The sulfate content was calculated using the following equation:  
\[\text{Sulfate content }(\text{mg}/\text{g})=\frac{\text{W2}}{\text{W1}}\times 1 00\times 0.4116\]
## 4 Experimentation and Results
### Retailer Characteristics
#### 4.1.1 Store Size  
The study's participating stores range from medium-sized businesses to major corporations. The retail sizes of the companies under study usually range from 500 to 1200 square meters. These shops are primarily found in metropolitan malls, guaranteeing a high customer volume. This variety of shop sizes makes it possible to analyze how object identification technology functions in various retail environments, ranging from compact, straightforward layouts to more extensive complex facilities with several parts and departments.
# Scenario-Based Configuration Refinement for High-Load Cellular Networks: An Operator View
## 5 High-Speed Scenario
### Frequency Band for Private Network  
Most cities near the high-speed railway in Jiangsu province deploy public networks with the \(F\) band. We investigate two frequency band policies for private networks. One is that private networks use the \(F\) band, i.e., 1895\(\sim\)1915 MHz. There exists an overlapping frequency band around 5 MHz between the private networks and neighboring public networks. The other is that the private networks exclusively use the \(D\) band without any overlapping frequency band.  
By jointly considering Figure 8 and Table 13, we observe that the average RSRP increases 4 dB, and the signal to interference plus noise ratio (SINR) decreases 3 dB. The average download data rate decreases 3 Mbps using the first strategy. However, the performance using the first strategy is affected by communication quality of the cell edge. The download data rate of edge areas with low coverage decreases 4\(\sim\)5 Mbps. We also realize that the performance using the second strategy is affected by signal quality at the cell edge. The percentage of area where the RSRP is lower than \(-\)110 dBm is 1.8%. In general, the private networks for high-speed railways are suggested to employ the \(D\) band if the public networks operate in the \(F\) band. The areas with low network coverage are suggested to operate in the \(F\) band.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Average RSRP** & **Average SINR** & **Downlink Data Rate** \\  & **(dBm)** & **(dB)** & **(Mbps)** \\ \hline strategy 1 & \(-\)93.19 & 11.42 & 21.34 \\ strategy 2 & \(-\)96.94 & 15.06 & 24.33 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Comparison between two frequency band strategies for high-speed railway.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Function** & **Preferred Value** \\ \hline Control format indicator (CFI) & Guarantee the resource of physical downlink control channel (PDCCH) & 3 \\  & Increasing UE inactivated timer can reduce the number of establishing RRCs; & \\ UE inactivated timer & decrease UE inactivated timer can reduce the number of online users during & 10 \\  & a certain period & \\ Access class barring (ACB) & Decreasing the value of the ACB factor can reduce the number of attempts to & \\  & establish RRC during a certain period & 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Access parameters related to signaling packet exchange.
## 2 Pointing Strategy and Model Establishment
### Disturbance Analysis and Descriptions
#### 2.3.2 Nonlinear Friction Torque  
During the operation of the telescope pointing mechanism, the contact and movement between various components can result in the presence of frictional forces. Frictional torque induces speed fluctuations in the system, causing phenomena, such as dead-zone crawling, nonlinearity, and steady-state errors when the system operates at low speeds. Considering the trajectory of the breathing angle, the telescope pointing mechanism typically operates at low speeds. The presence of nonlinear friction can prevent the telescope pointing system from meeting high-precision pointing requirements. To enhance the control performance of the system, it is necessary to reduce or eliminate the impact of friction on speed stability.  
The LuGre model can accurately and comprehensively describe the static friction characteristics and the presence of dynamic friction phenomena in the telescope pointing mechanism [22]. This model employs an elastic bristle u to simulate the structure of the contact surface, and the frictional torque can be represented as:  
\[\begin{array}{l}\mathrm{M}_{\mathrm{T}}^{f}=\lambda_{0}z+\lambda_{1}\dot{u }+\lambda_{2}v\\ \dot{u}=v-u\big{|}v\big{|}/g(v)\\ \lambda_{0}g(v)=F_{\mathrm{c}}+(F_{\mathrm{s}}-F_{\mathrm{c}})e^{-(v/\eta_{ \mathrm{s}})^{2}}\end{array}, \tag{10}\]  
where \(\lambda_{0}\) is the stiffness coefficient, \(\lambda_{1}\) is the damping coefficient, \(\lambda_{2}\) is the velocity-dependent damping between the contact surfaces, \(F_{\mathrm{c}}\) is the coulomb friction force, \(F_{\mathrm{s}}\) is the static friction force, \(v\) is the relative velocity between the contact surfaces, vs. is the Stribeck velocity, \(u\) is the average deformation of the contact surface asperities, and \(g(v)\) is a function related to the variable \(v\). According to Equation (10), the friction force is a disturbance related to the rotational speed of the pointing mechanism.
# MIDOM--A DICOM-Based Medical Image Communication System
## 1 Introduction
The system consists of a server component used as the hub for storing diagnostic data and providing functionalities to create studies (batches of diagnostic data), consultation requests, and responses. The system features two versions of a **C**lassification and **B**lending **P**redictor **C**oder (CBPC) lossless compression method that we developed as part of our previous research [7; 8]. Depending on the client-side parameters, such as connection bandwidth, display size, and storage capacity, the exchange of consultation requests can be completed with or without the CBPC compression. The contributions of the paper are as follows:  
1. A framework for exchange of DICOM-based medical images between remote medical professionals in heterogeneous environments with closed and isolated PACS systems and varying connectivity;
2. Reduction of transmission latency and cost through integration of our customized lossless image compression method (CBPC) into the system in question;
3. A system for the increase in the availability of medical care in underdeveloped parts of the world, dependent on low-grade mobile networks. The rest of the paper is organized as follows. First, in Section 2 we provide the background needed to understand the paper completely, through theoretical concepts and related work. In Section 3, we describe the implementation of the MIDOM system, together with the integration of our custom compression method. Next, in Section 4 we describe the testing methodology and benchmarks, and present the results in terms of the compression ratio and latency reduction in the selected network environments. Section 5 finally concludes the paper.