esa-sceva/satcom-chunk-collection huggingface dataset:
# VLC Network Design for High Mobility Users in Urban Tunnels
## 6 Results and Discussion
### Discussion  
Our experimental simulations evaluate the performance of a vehicular VLC network in a tunnel scenario for single and multiple vehicles. The results provide further knowledge about the behavior of the VLC network in realistic use-case scenarios for vehicular users and a promising proposed handover mechanism that copes with high mobility. However, the network's performance can be affected by the number of users due to the shared medium. But, this is a common issue that both technologies have to deal with (VLC and DSRC). It is demonstrated in [64] that DSRC suffers similar drawbacks when the vehicle density grows. The packet delivery ratio diminishes in DSCR under these conditions. Moreover, a fair comparison of the performance degradation for these two technologies is difficult due to their remarkable differences. On the one hand, VLC cell does not require sharing resources with multiple vehicular nodes, while DSRC cell provides service to a wide area. Thus, it has a channel used by a large number of nodes. On the other hand, a VLC network will require more frequent handovers to provide service. This action will also limit the service quality.  
Despite its disadvantages, the proposed VLC handover strategy appears as a promising scheme to support communication for some vehicular scenarios. As mentioned before, these are initial studies with several issues that remain unsolved. In this work, we designed a realistic vehicular tunnel scenario to evaluate the operation of the proposed VLC network. However, it is not a straightforward process to adapt our design to other environments. For example, the detection algorithm is simple and takes advantage of the scenario geometry and the limited vehicle's degree of freedom. So, this algorithm can not be used in other urban scenarios, such as streets.  
On the other hand, the 2.5 layer topology is a solution that has the potential to solve the communication in other indoor scenarios, such as offices. Finally, the developed simulation platform can evaluate some other mobility scenarios for VLC users. For instance, a vehicle moving through a random trajectory or a VLC gadget operating inside an office. Although, it is necessary to design detection strategies for each.
## 3 System Model
### Computing Model
#### 3.5.1 UAV Computing Latency  
When IoTDs choose to offload their MLLM tasks to UAVs through wireless connections, the communication latency model encompasses both the upload transmission latency and the latency associated with returning the results. In this context, the term \(T_{k}^{UAV}\) represents the total UAV computing latency, which includes the latency needed to upload multimodal data from IoTD \(k\) to UAV \(m\), the inference processing latency at UAV \(m\), and the transmission of the resulting data back to IoTD \(k\). For a specified volume of multimodal input data \(u_{k}(t)\), the upload transmission latency can be calculated by  
\[T_{k,m}^{up}(t)=\frac{u_{k}(t)}{R_{k,m}^{up}(t)}. \tag{12}\]Hence, the energy consumption of upload transmission from IoTD \(k\) to UAV \(m\) is  
\[E_{k,m}^{up}(t)=P_{k}(t)T_{k,m}^{up}(t). \tag{13}\]  
Upon acquiring multimodal data, UAV \(m\) is able to handle the MLLM inference request for IoTD \(k\). The inference execution duration, denoted as \(T_{k,m}^{ex}(t)\), is influenced by the maximum floating-point operations per second (FLOPS) capacity of the onboard computing capability of UAV \(m\), in addition to the total computational resources required for the inference process. Consequently, the computational latency for the MLLM on UAV \(m\) to generate content for IoTD \(k\) can be expressed as  
\[T_{k,m}^{exe}(t)=\frac{v_{m}\cdot o_{k}(t)}{f_{m}^{spu}}, \tag{14}\]  
where \(v_{m}\) denotes the number of FLOPS used by one inference step of MLLM on UAV \(m\) per output word, and \(f_{m}^{spu}\) is the FLOPS capability of UAV \(m\).  
The energy consumption associated with the inference task of IoTD \(k\) on UAV \(m\) is expressed as  
\[E_{k,m}^{exe}(t)=P_{m}^{exe}T_{k,m}^{exe}(t), \tag{15}\]  
where \(P_{m}^{exe}\) denotes the power consumption of UAV \(m\)'s processing unit.  
During the result return phase, the output of the MLLM task for IoTD \(k\) is determined by multiplying the average data size per output word, denoted as \(d^{token}\) (in bits per word), by the total number of output words. Consequently, the downlink transmission latency is defined as  
\[T_{k,m}^{dl}(t)=\frac{d^{token}o_{k}(t)}{R_{k,m}^{dl}(t)}, \tag{16}\]  
where \(R_{km}^{dl}(t)\) represents the downlink transmission rate between IoTD \(k\) and UAV \(m\).  
The energy consumption associated with the downlink transmission from UAV \(m\) to IoTD \(k\) is computed as  
\[E_{k,m}^{dl}(t)=P_{m}T_{k,m}^{dl}(t), \tag{17}\]  
where \(P_{m}\) represents the transmission power of UAV \(m\).  
In summary, the total UAV computing latency for IoTD \(k\) on UAV \(m\) at time slot \(t\) is expressed as  
\[T_{k,m}^{UAV}(t)=T_{k,m}^{up}(t)+T_{k,m}^{exe}(t)+T_{k,m}^{dl}(t). \tag{18}\]  
Thus, the total energy consumption for the MLLM task of IoTD \(k\) processed on UAV \(m\) is calculated as follows.  
\[E_{k,m}^{UAV}(t)=E_{k,m}^{up}(t)+E_{k,m}^{exe}(t)+E_{k,m}^{dl}(t). \tag{19}\]
## 2 Methodology
The exclusion criteria applied are as follows:  
EC 1: Papers that are not related to the intended topic based on reading the title and abstract  
EC 2: Duplicate studies and studies with similar topics and findings  
EC 3: Comparison papers and review papers  
Figure 1: Review protocol (adapted from [PERSON] et al. (2016)). EC 4: Papers that are not relevant to the intended purpose of this study based on reading the paper content  
In the first exclusion (EC1), we excluded papers that were not related to the use of technology in digital addiction by reading the abstract. This stage resulted in reducing the number of papers to 36. The second exclusion (EC2) was performed in which papers that belong to the same study and papers with similar themes and findings were removed for duplication. After applying these exclusions, 27 papers have remained. The third exclusion excluded One comparison paper and One review paper. There are 25 papers left from this exclusion. The fourth exclusion excluded papers that were considered irrelevant to the intended purpose of this study after a thorough reading of the papers' content. This process excluded two papers and resulted in 23 papers left. The peer-review process was then conducted to validate the selection process. The 23 papers left were then screened by assessing its quality. We determine the quality criteria adopted from [PERSON] and [PERSON] [37] for assessing the quality of the papers. A dichotomous (\"yes\" or \"no\") scale was used to grade each of the criteria. Based on the assessment results and discussion between researchers, of the 23 articles assessed for quality, 8 papers were excluded. The remaining 15 papers (study A-O) were left for further extraction and synthesis. The overall literature search process is shown in Figure 2.  
Once the final studies were identified, the data were extracted from each of the 15 studies. All the information needed to answer the research questions were retrieved in the data extraction form. The data extraction form included a set of attributes such as identification number of the study, authors of the study, publication year, publication platform, publication type, country, the domain of the topic in digital addiction, research goal, the IT used or proposed in the study, research method, and findings. The data extracted from these 15 papers (study A-O) can be seen in Table 1. The extracted data were then synthesized. In this stage, data extracted from primary studies were summarized and research questions were answered. We implemented a qualitative synthesis of the extracted data. The synthesis results are provided in the next section. Figure 2: Stages of primary studies search. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Study ID** & **Year** & **Citation** & **Country** & **Category** & **Domain** & **Goal** & **Method** & **Findings** \\ \hline \hline \multirow{8}{*}{A} & \multirow{8}{*}{2017} & \multirow{8}{*}{[PERSON] et al.
# Microbiological and Sensory Quality of Artisanal Sour Cream
###### Abstract  
Following hygiene standards in milk production is essential for making high-quality sour cream, especially when using traditional methods that rely on raw milk. The aim of this study was to determine the physicochemical, microbiological, and sensory quality of artisanal sour cream samples collected from major marketplaces in the wider Zagreb area. On average, the samples contained 27.99% milk fat, 3.30% protein, 34.29% dry matter, 6.51% fat-free dry matter and 3.00% lactose, with considerable variability observed across all components. Microbiological analysis revealed the presence of _Staphylococcus aureus_ in 35.30% of the samples, _Enterobacteriaceae_ in 76.47%, _Escherichia coli_ in 94.11%, _Bacillus_ spp. in 23.53%, and yeasts in 100% of the samples. _Listeria monocytogenes_ and _Salmonella_ spp. were not detected. The sensory analysis of the textural properties showed significant variability in firmness, adhesiveness, viscosity, creaminess, and fizziness. Samples with higher milk fat and dry matter content were rated better for creaminess, viscosity and mouth firmness. Flavour assessments, particularly for cream and diacetyl notes, also varied widely among samples. These findings highlight the complexity of sour cream's sensory attributes and the significant influence of ingredient composition and processing techniques on appearance, aroma, texture, taste, and flavour. Principal component analysis (PCA) with Varimax rotation simplified the data structure and identified key dimensions of quality variation. Principal component analysis (PCA) revealed that the first principal component (PC1) effectively discriminated the cream samples based on sensory attractiveness and indicators of spoilage and highlighted the association between off-flavour and microbial contamination with inferior characteristics. The second principal component (PC2) captured the differences in physicochemical characteristics and showed a gradient from richer, creamier samples with higher fat content to those with lower acidity and higher freshness.  
sour cream; hygiene standards; microbiological quality; physicochemical properties; sensory analysis +
Footnote †: journal: Applied sciences
# Internal Flow Prediction in Arbitrary Shaped Channel Using Stream-Wise Bidirectional LSTM
## 4 Results and Discussion
### Quantitative Evaluations
#### 4.3.2 Mean Absolute Error along Height Axis in Entire Channel  
To analyze the characteristics of the errors of the models in Section 4.2, the mean absolute error along the height axis was calculated for each width position for the entire test data set using Equation (7).  
\[MAE(\alpha,i)=\frac{1}{N}\sum_{l=1}^{N}\sum_{j=1}^{Ny}|\alpha_{ij}-\hat{\alpha}_{ ij}|\delta_{ij}. \tag{7}\]  
The calculated results are visualized in Figure 15. In the graph, U-Net with SB-LSTM shows the lowest error at all width positions, followed by ED with SB-LSTM, which has the next lowest error. Regardless of the presence or absence of the SB-LSTM module, the error of all models tends to increase as the predicted regions move further away from the boundary, with the boundary condition set to a fixed value. More attention should be given to models trained without the SB-LSTM module because of the periodic and significant increase in error in the stream-wise case. These peaks occur with exactly the same spacing and position, regardless of whether they are close to or far from the fixed boundary conditions in all width ranges of \(u\), \(v\), and \(P\). This indicates that the error observed in Section 4.2 is occurring throughout the entire area, suggesting a problem with the approach of learning using a fully connected NN. However, when the SB-LSTM module is added, the error increases linearly without sharp changes. This confirms that the SB-LSTM module helps to address the issues that arise during the training of internal flows with the existing model.
# Survivable Deployments of Optical Sensor Networks against Multiple Failures and Disasters: A Survey
###### Abstract  
Optical sensing that integrates communication and sensing functions is playing a more and more important role in both military and civil applications. Incorporating optical sensing and optical communication, optical sensor networks (OSNs) that undertake the task of high-speed and large-capacity applications and sensing data transmissions have become an important communication infrastructure. However, multiple failures and disasters in OSNs can cause serious sensing provisioning problems. To ensure uninterrupted sensing data transmission, survivability has always been an important research emphasis. This paper focuses on the survivable deployment of OSNs against multiple failures and disasters. We first review and evaluate the existing survivability technologies developed for or applied to OSNs, such as fiber bus protection, self-healing architecture, and 1 + 1 protection. We then elaborate on the disaster-resilient survivability requirement of OSNs. Moreover, we propose a new \(k\)-node (edge) sensing connectivity concept, which ensures the connectivity between sensing data and users. Based on \(k\)-node (edge) sensing connectivity, the disaster-resilient survivability technologies are developed. The key technologies necessary to implement \(k\)-node (edge) sensing connectivity are also elaborated. Recently, artificial intelligence (AI) has developed rapidly. It can be used to improve the survivability of OSNs. This paper details potential development directions of survivability technologies of optical sensing in OSNs employing AI.  
network survivability; disaster-resilience; optical sensor networks (OSNs); optical sensing; optical networks; \(k\)-node (edge) sensing connectivity; artificial intelligence (AI)
# The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review
## 3 Handwritten Chinese Character Recognition
### Deep Learning Based Recognition Methods
#### 3.2.2 Create Fast and Compact Models  
Networks tend to be larger and deeper to achieve higher recognition accuracy. However, with limited computational resources, researchers must consider the computational cost and memory consumption of the model.  
The issues of model recognition speed and storage capacity in HCCR tasks were addressed by [PERSON] et al. [54] in 2017 by proposing a Global Supervised Low-Rank Expansion (GSLRE) method and an Adaptive Drop-Weight (ADW) strategy to build faster and more compact CNNs. Compared to the baseline model, their proposed method loses only a fraction of a percent of accuracy, but computational cost and storage parameters can be reduced by a factor of ten or more. In 2018, [PERSON] et al. [55] improved Global Average Pooling to address the problem of simply summing spatial information with the same weights at the expense of accuracy degradation. They added an attention mechanism to the Global Average Pooling method, called Global Weighted Average Pooling (GWAP), so that the fully connected layer parameters are reduced without the cost of accuracy degradation. In 2020, [PERSON] et al. [56] made improvements to the GWAP proposed in the literature [55]. They proposed a high-performance identification network with interpretability. The network can learn the in-depth feature information and can visualize it. In addition, they introduced Global Weighted Output Average Pooling (GWOAP) to improve the performance of the model. In the same year, [PERSON] et al. [57] proposed CWCCNN-V1, a neural network consisting of the encoder and the classifier, which has a smaller depth and fewer parameters with guaranteed accuracy. [PERSON] et al. [58] proposed a lightweight network named LightweightNet for HCCR. The network structure uses the low-dimensional features of the fully connected layer to save a large amount of memory. It constructs multiple compact modules to extract the features of the convolution layer and uses the extraction rules based on accuracy sensitivity to save the calculation time. The experimental results also proved the model's advantages in speed and performance. Inspired by the lightweight network shuffleNetV2, [PERSON] et al. [59] recently proposed an efficient network model combining the Multiple scale convolution shuffle module (MSCS) for acquiring in-depth features of images and the attention features spatial aggregation (ASA) module for compressing important features. The method was tested at ICDAR 2013, requiring only 22.9 MB of storage, and achieving an accuracy of 97.63%.
# Advancing Digital Twin-Based Collision Avoidance:
## 4 Functional Safety for Wireless Systems
### Modeling Outage Frequency for Wireless Systems
#### 4.3.1 Bernoulli Model for \(PFH_{\mathrm{D}}\)  
The probability for each packet to be lost is given as \(p\). In a scenario where losing \(k\) packets in a row would lead to a dangerous situation \(D\), the probability for a loss of length \(k\) is given by [23] as \(p_{k}=p^{k-1}\cdot(1-p)\) if packets are transmitted with a frequency \(f\) and we receive the \(PFH_{\mathrm{D}}\) as follows:  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Burst Length \(k\)** & **0** & **1** & **2** & **3** & **4** & **5** \\ \hline Trace [23] Count \(m_{k}\) & 9992 & 469 & 144 & 24 & 7 & 5 \\ Trace Section 3 Count \(m_{k}\) & 107,558 & 203 & 8 & 1 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Burst loss count for two data sets.  
\[PFH_{D}=p^{k}\cdot(1-p)\cdot f\cdot 3600\,\mathrm{s} \tag{5}\]  
For a safety function, we assume that there is a duration \(T\), so that the system is no longer safe if an outage with duration \(\geq T\) occurs. Since it is \(k=T\cdot f\), for a system with frequency \(f\), we can generalize the equation to  
\[PFH_{D}=p^{T.f}\cdot(1-p)\cdot f\cdot 3600\,\mathrm{s} \tag{6}\]  
Let us consider a scenario where the wireless communication has a probability of 99% to transmit a message successfully. For this scenario, we assume that a safety function prevents accidents. If it does not receive the messages in time \(T\), the safety function fails and a dangerous accident can no longer be prevented. In Figure 9, the x-axis represents the frequency with which messages are sent. The y-axis represents the probability of a dangerous situation arising. The colored areas in the plot show the reached performance level. In this case, we can see that in a scenario where \(T=1\,\mathrm{s}\), we need a message frequency of more than \(5\,\mathrm{Hz}\) to reach PL A and more than \(6\,\mathrm{Hz}\) to reach PL D.  
Given that PL pertains to a specific safety function, it is essential to ensure its efficacy precisely at the point of application. While this requirement is negligible for wired connections, where signals behave uniformly, it is crucial for wireless signals due to environmental variations. In wireless scenarios, factors such as obstacles between the sender and receiver can significantly affect transmission. Therefore, to guarantee a specific PL, it is necessary to measure the outage probability for each location where the function is employed.  
However, in this paper we mainly consider vehicular applications where sensors might also be applied to vehicles, especially forklift trucks. In industrial areas, forklift trucks are used to stack large objects which then block the line of sight, weakening the signal and making it more error prone. So, the signal is always variable.
# On the Rollout of Network Slicing in Carrier Networks: A Technology Radar
## 3 Impact of Network Slicing
### RAN Slicing  
In the NG-RAN, the introduction of slicing has an impact on three main aspects: gNB configuration, mobility support and Radio Resource Management (RRM) procedures.
## 2 Materials and Methods
### Experimental Setup and Data Process  
Five subjects participated in the experiment (all male, aged \(27\pm 2.64\) years, weight \(61.7\pm 7.4\) kg, height \(1.70\pm 0.58\) m). Written informed consent was given, and the study was approved by the ethics committee of Wuhan University of Technology. The subjects were instructed to maintain upright standing on a force plate. The two feet were slightly separated, within the shoulder width, at about 20 cm apart. Disturbances were exerted on their backs in the form of pushing by another participant. A trigger button that was connected to data collection device (NI-DAQmx, National Instrument, Austin, TX, USA) to record the pushing time was attached to the participants' backs. The pushing time was arbitrarily selected by the pusher, but with a minimum of 6 seconds break in between. The push was restricted to within 0.5 s to avoid large a disturbance evoking other strategies. After three trials, there was a 10-min break. Every subject repeated the experiment twice. Subjects practiced several times to gain familiarity with the experimental protocol.  
An inertial measurement unit (IMU) sensor (MTI-300-2A5G4, Xsens Technologies B.V., Enschede, NED) was attached to the shank to record the ankle joint kinematics data (i.e., joint angle, angular velocity and linear acceleration, with a sampling frequency of 300 Hz). A force plate (AMTI BP400600, Advanced Mechanical Technology, Inc., Denver, CO, USA) with a sampling frequency of 1000 Hz was used to record ground reaction forces (GRFs) and calculate COP positions. The NI-DAQmx was used to record the six-channel analog outputs of the force plate. As the pushing trigger signal was also recorded by NI-DAQmx, the pushing time and force plate data were recoded synchronously. sEMG data were recorded from three muscles, namely, tibialis anterior (TA), gastrocnemius (GAS) and soleus (SOL) with a commercial apparatus (DataLog MWX8, Biometrics, Ladysmith, VA, USA). TA sEMG signals were recorded as references. The sEMG electrode attachment procedure followed the instructions from SENIAM. The sampling frequency of the sEMG was 1000 Hz, with differential amplification (gain: 1000) and common mode rejection (104 dB). Maximum voluntary contraction (MVC) tests were performed before the experiment, and used for sEMG signal normalization. Custom software was developed with VS 2010 (Visual Studio 2010, Microsoft Corporation, Redmond, WA, USA) to the fulfill data collection operations. A laptop was sufficient to run the software in real-time. The experimental setup is shown in Figure 4.  
Analysis of variance (ANOVA) was performed to test the difference in VAF among different time points. T-test was used to examine differences in reflex gains among subjects and VAF of reflex model prediction results with and without acceleration gains.
## 2 Materials and Methods
### Methodology  
#### 2.2.1 Temporal and Spatial Collocation  
Temporal consistency between GK2A satellite observations and ground-based visibility measurements was ensured by synchronizing the visibility data using the median of five 1 min interval values collected between min 00 and 04 past the hour. This method accounts for the delay of approximately 2-3 min in GK2A observations over the Korean Peninsula following the start of each full-disk scan cycle.  
Spatial colocation was achieved by extracting GK2A fog detection values from the satellite pixel nearest to each visibility meter site. Fog detection results from a 3 \(\times\) 3 pixel neighborhood centered on the nearest pixel (i.e., the 3 \(\times\) 3 neighborhood pixel method) were also utilized to compensate for potential satellite geolocation errors and to improve spatial representativeness by accounting for local variability around the observation site.