esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Onboard Cellular Network  
The onboard cellular network was implemented by the evolved Azcom Small Cell platform [32], consisting of an integrated system (the Smart Cell in Figure 4). At the heart  
Figure 8: CTD in the red dashed oval placed on the container.  
Figure 7: 3D CAD model of the CTD’s board.  
of this setup is the Base Band Unit (BBU), which incorporates a System on a Chip (SoC) responsible for managing the entire LTE/NB-IoT protocol stack, in accordance with the standards defined in 3 GPP Release 13. This integration allows the system to handle complex processing tasks, such as signal modulation, resource allocation, and error correction, ensuring smooth and efficient data transmission across the network.  
Complementing the BBU is the Analog Front End (AFE), which is responsible for generating the RF (Radio Frequency) signal within the designated LTE frequency band optimized for NB-IoT communication. The AFE ensures that the analog signals are transmitted with the necessary clarity and strength, enabling reliable connectivity in diverse environments, including remote or congested areas. The RF module was developed to operate in the 700 mHZ band (B28), to maximize onboard radio coverage, with an in-band carrier and external antenna.  
Powering the entire system is the Power Supply Module (PSM), designed to provide stable and continuous energy to all components. This module is crucial for maintaining uninterrupted network operations, particularly in scenarios where consistent power delivery is critical for maintaining communication links.  
Finally, the architecture includes a computation unit dedicated to executing the Evolved Packet Core (EPC) functions. The EPC is a central part of the network, managing core tasks such as user authentication, data routing, and connection management. This ensures seamless integration between the radio access network and external networks, facilitating efficient data flow and maintaining overall network stability. This proprietary EPC, compliant with the 3 GPP R13 standard, includes the following entities and related interfaces configured as Network In a Box [33]:  
* Mobility Management Entity (called V-MME)
* Serving Gateway (called S-GW lite)
* PDN (Packet Data Network) Gateway (called P-GW lite)
* Local Home Subscriber Server (HSS)  
Together, these components form a cohesive system that supports advanced LTE and NB-IoT communication, providing robust connectivity solutions essential for modern IoT applications and services.  
The system was designed to be suitable with environmental protection class IP67 for the outdoor environment, like onboard the ship.
## 6 Conclusions  
In this paper, a single-end positioning method of modulus wave velocity difference based on MPA-optimized MVMD and morphological gradient is proposed. Through simulation and analysis, the following conclusions are obtained:  
1. Taking the minimum comprehensive entropy kurtosis ratio as the fitness function, the traveling wave characteristic signals with the most significant travel wave characteristic information and the lowest noise component can be decomposed. Then, the singularity of the wave head is further enhanced by the morphological gradient value, and the accurate location of the fault point is realized.
2. Using MVMD to process both the line-mode traveling wave and zero-mode traveling wave at the same time can effectively shorten the extraction time of the traveling wave characteristic signals on the basis of retaining the advantages of VMD noise interference robustness without being affected by modal aliasing.
3. The proposed method has strong adaptability to different types of line faults, different initial fault conditions, and noise interference. Compared with the VMD-TEO method and the EMD_first-order difference method, the accuracy of the positioning results of this method is the highest, and the algorithm time is significantly lower than that of the VMD-TEO method.  
Conceptualization, L.W. ([PERSON]); Methodology, Y.Z.; Validation, Y.W.; Formal analysis, J.C.; Data curation, Y.Z.; Writing--original draft, J.C.; Writing--review & editing, Y.W., L.W. ([PERSON]) and Y.Z.; Supervision, L.W. ([PERSON]); Project administration, L.W. ([PERSON]) and [PERSON] ([PERSON]); Funding acquisition, L.W. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by \"the Fundamental Research Funds for the Central Universities (Ph.D. Top Innovative Talents Fund of CUMTB)\". Grant number: BBJ2024066.  
The original data presented in this study are included in the manuscript. Further enquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company Dating North China Electric Power Test and Research Institute, China Datang Corporation Science and Technology General Research Institute Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Effects of Viewing Displays from Different Distances on Human Visual System
###### Abstract  
The current stereoscopic 3D displays have several human-factor issues including visual-fatigue symptoms such as eyestrain, headache, fatigue, nausea, and malaise. The viewing time and viewing distance are factors that considerably affect the visual fatigue associated with 3D displays. Hence, this study analyzes the effects of display type (2D vs. 3D) and viewing distance on visual fatigue during a 60-min viewing session based on electroencephalogram (EEG) relative beta power, and alpha/beta power ratio. In this study, twenty male participants watched four videos. The EEGs were recorded at two occipital lobes (O1 and O2) of each participant in the pre-session (3 min), post-session (3 min), and during a 60-min viewing session. The results showed that the decrease in relative beta power of the EEG and the increase in the alpha/beta ratio from the start until the end of the viewing session were significantly higher when watching the 3D display. When the viewing distance was increased from 1.95 m to 3.90 m, the visual fatigue was decreased in the case of the 3D-display, whereas the fatigue was increased in the case of the 2D-display. Moreover, there was approximately the same level of visual fatigue when watching videos in 2D or 3D from a long viewing distance (3.90 m).  
3D display; visual fatigue; electroencephalogram (EEG); causative factor +
Footnote †: journal: Applied Sciences
# Active Clamp Boost Converter with Blanking Time Tuning Considered
## 3 Design Considerations
### Design of Resonant Inductance \(L_{r}\)  
After the resonant capacitance \(C_{s}\) is determined, the design of the resonant inductance \(L_{r}\) will follow. As the resonant radian frequency \(\omega_{1}\) is desired not to affect the operation behavior of the converter, the value of \(\omega_{1}\) ten times larger than the value of the switching radian frequency \(\omega_{s}\), namely, \(\omega_{1}>2\pi\times 10^{6}\text{rad}/\) sec. Therefore, based on Equations (43) and (44), the inequality of \(L_{r}\) can be obtained to be  
\[L_{r}<\frac{1}{2\times\left(2\pi\times 10^{6}\right)^{2}\times C_{s}}=\frac{1}{2 \times\left(2\pi\times 10^{6}\right)^{2}\times 10^{-9}}=12.6\ \upmu\text{H} \tag{44}\]Accordingly, the value of \(L_{r}\) is chosen to be 10 \(\mu\)H. After this, a T175-18 core, manufactured by ARNOLD Co., is adopted, which has an inductance coefficient \(A_{L1}\) of 75 nH/N\({}^{2}\), and hence the number of turns, called \(N_{r}\), is  
\[N_{r}=\sqrt{\frac{L_{r}\times 1000}{A_{L2}}}=\sqrt{\frac{10\times 1000}{75}}=11.55 \tag{45}\]  
Finally, based on Equation (45), the value of \(N_{r}\) is chosen to be 11.
# CoFi: Coding-Assisted File Distribution over a Wireless LAN
## 7 Conclusions  
In this paper, we introduced CoFi, a MAC protocol that improves Wi-Fi performance under harsh channel conditions. To mask packet losses while ensuring reliable delivery, CoFi groups packets into batches, and continuously transmits random linear combination of all data in each batch, until they can be decoded by the receiver. In addition, CoFi allows clients to cache overheard packets, and schedules the best relay to help the client with weak link quality to the access point. Using trace based simulation, we find that CoFi can significantly improve throughput in a lossy network, and gracefully degrade to a non-coding protocol when the channel condition is good. The per-packet delay of CoFi is comparable to 802.11 in a lossy network, and even better in a dynamic topology with mobile clients. We also developed a simple analytical model that justifies the performance of CoFi from a fairness point of view. Our ongoing work tries to improve CoFi from the following aspects:  
* Implementation of CoFi on real wireless nodes. We plan to implement CoFi on the USRP testbed, which will provide more accurate experimentation results. We are also considering making CoFi more compatible with existing 802.11 features, such as rate adaptation and service differentiating.
* CoFi for elastic applications. In this paper, we assumed stable application data when evaluating CoFi and 802.11. Elastic traffic, such as VBR, has more stringent requirements on delay performance. To adapt to such applications, we have to allow a dynamic batch size, so that each packet can be delivered on time. While adjusting the batch size, we also need to consider the end-host delay of network coding, especially when the computation load of coding operations is non-negligible.  
[PERSON] contributed to derive the results and write the paper. [PERSON] proposed the idea and supervised the work. He also revised the article.  
The work described in this paper was partially supported by the National Natural Science Foundation of China under Grant (No. 51474100) and the Natural Science Foundation of Heilongjiang Province under Grant (No. QC2016093).  
The authors would like to express their appreciation of associate professor [PERSON] and [PERSON] for their valuable suggestions. The authors would also like to thank the anonymous reviewers for their constructive comments.  
The authors declare no conflict of interest.
## 6 Experimental Results
### DM Conducted Emissions  
Since the oscillations affecting \(i_{\text{sw}}\) at the \(T_{\text{HS}}\) turn on are strongly reduced with the proposed technique, as shown in Figure 8, and since the main source of DM conducted emissions is the switching current \(i_{\text{sw}}\) itself, then the proposed technique is expected to reduce the DM EMI too. Aiming to assess such a point, the DM current was evaluated as  
\[i_{\text{DM}}(t)=\frac{v_{\text{LISN,p}}(t)-v_{\text{LISN,n}}(t)}{2R_{\text{DM}}}, \tag{10}\]  
where \(v_{\text{LISN,p}},v_{\text{LISN,n}}\) are the voltages acquired by a digital oscilloscope at the measurement ports of the two LISNs loaded with 50 \(\Omega\) and \(R_{\text{DM}}=\) 100 \(\Omega\) is the equivalent DM mode resistance [33]. The frequency spectrum of \(i_{\text{DM}}\) was evaluated, resulting in the spectra shown in Figure 17 for the oscillating and the 6.6 nH source inductance inserted. From such spectra it can be noticed that the proposed technique allows one to reduce the turn-on peak of the DM EMI by 15 dB, which is a noteworthy result.
# LaRaCog: A Protocol for Cognitive Radio-Based LoRa Network
## 8 Conclusions  
In this paper, we proposed a new protocol for Cognitive Radio-based LoRa network, which is called LoRaCoog. LoRaCoog ensures the fair usage of spectrum and maintains suitability for battery-operated devices. Furthermore, it enhances the communication between EDs and GWs accordingly. In fact, it is a complementary protocol for LoRaWAN used when the spectrum is congested or limited.  
LoRaCog is inspired by LoRaWAN by implementing CR to manage spectrum sensing and available channel decisions. This will be accompanied with changes to the ED RX2 receive window. The GWs should have modifications in hardware and software to empower spectrum sensing as suggested earlier and the NS to embed the capability to authorize the usage of the least used channels after receiving the sensing results from the GWs. These modifications will have no impact on battery consumption for EDs, since sensing is done by GW and hence defeats the main challenge of CR in high power consumption.  
Then, we configured our system model to compare LoRaWAN and LoRaCog in different scenarios. The results show the efficiency of LoRaCog compared to LoRaWAN. It improves the QoS and decreases collisions. It also allows us to decrease the number of GWs and thus the network cost. For future work, we will be looking at the GWs hardware to manage spectrum sensing over different bands and dynamically move within the spectrum. In addition, machine learning techniques are added to the NS for deciding on the best available channels in a short time by looking at historical activities of primary users and the EDs' expected transmission time to avoid any collision or service disturbance.  
All authors ([PERSON], A.N., M.M., [PERSON] and A.M.) have contributed equally in this research. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 13: LoRaWAN 868 MHz vs. LoRaCog 868/438 MHz with PU re-appearance probability = 10%.
# Improving Energy Efficiency on SDN Control-Plane Using Multi-Core Controllers
## 3 Materials and Methods
### Working Frequency versus Parallelism  
SDN controllers are responsible not only for the packet forwarding decisions but also for tasks related to security, accountancy and administration. The very nature of the multiple functions a controller has to do in a real-world scenario implies the need and leverage of parallel multicore processors. It is better to deliver these tasks as fast as possible, but this may imply higher energy use. The network has its bandwidth limits, and there is a maximum frequency when a faster processor will be idle waiting for the network limitation on delivering new packets, thus wasting energy.  
In an optimized solution, the processor should adapt itself to the need of the network, lowering its energy consumption while keeping with the expected parameters of the network, namely throughput, latency, packet drop, jitter, etc. To demonstrate the impact of the processor frequency on the throughput of an SDN controller, an experiment was conducted: the same load was applied to a single-core controller with changing operation frequency from 1.6 to 3.0 GHz.  
Figure 6 clearly shows that processor frequency and throughput have an almost linear relation for a single-core controller when the controller needs to process many packets per second. This conclusion that higher processor frequency leads to higher energy consumption, as stated by [PERSON] et al. (2016), leads to an analysis into the right balance between throughput and energy consumption to fulfill a well-defined level of service for an SDN.
## 4 Control Design
### Design of a Tracking Controller  
The control objective is to make sure that the tracking errors quickly converge to zero under the conditions of model parameter uncertainties, time-varying external disturbances and payload. First, the tracking errors of the system are defined. The vector of end-effector tracking errors is shown in (16), and the vectors of tracking errors in the joint space are shown in (17)-(19).  
\[\mathbf{e}_{E}=\hat{x}_{E}-\mathbf{x}_{Ed} \tag{16}\] \[\mathbf{e}=\begin{bmatrix}\mathbf{R}_{1}^{B}(\mathbf{\hat{\eta}}_{1}-\mathbf{\eta }_{1d})\\ \eta\mathbf{e}_{d}-\eta\mathbf{e}_{d}+\mathbf{S}(\mathbf{\varepsilon})\mathbf{e}_{d}\\ \hat{\mathbf{q}}-\mathbf{q}_{d}\end{bmatrix}\] (17) \[\dot{\mathbf{e}}=\mathbf{\hat{\xi}}-\mathbf{\zeta}_{d}\] (18) \[\ddot{\mathbf{e}}=\mathbf{\hat{\xi}}-\mathbf{\hat{\xi}}_{d} \tag{19}\]where the superscript \((\hat{\cdot})\) denotes the corresponding estimated values via EKF, and the subscript \((\cdot)_{d}\) denotes the corresponding desired values. \([\epsilon,\eta]\) and \([\epsilon_{d},\eta_{d}]\) are the quaternions of \(\hat{\eta}_{2}\) and \(\eta_{2d}\).  
Then, the tracking controller based on feedback linearization is given as  
\[\mathbf{u}=-\mathbf{M}^{*}(\mathbf{k}_{1}\mathbf{e}+\mathbf{k}_{2}\hat{\mathbf{e}}-\hat{\mathbf{\xi}}_{d})+ \mathbf{C}^{*}\hat{\mathbf{\xi}}+\mathbf{D}^{*}\hat{\mathbf{\xi}}+\mathbf{g}-\mathbf{J}^{T}\mathbf{k}_{E} \epsilon_{E}-\hat{\delta} \tag{20}\]  
where \(\mathbf{J}=\mathbf{J}(\mathbf{R}_{B}^{I},\mathbf{q})\) is the Jacobian matrix of the system, as given in (1). \(\mathbf{k}_{1}\), \(\mathbf{k}_{2}\) and \(\mathbf{k}_{E}\) are the positive symmetric matrices. \(\hat{\delta}\) is the estimated vector of the lumped uncertainties and disturbances, which is described in the next subsection.
## 3 Measurement Setup
### Test Sites  
We conducted measurements at five nearby Shenandoah Valley Railroad (SVRR) crossings, a Class 3 short-line railroad in Staunton, Virginia, U.S. As shown in Figure 3, the SVRR tracks cross a small town, and the environment near the crossings has vehicular traffic, numerous buildings, trees, and parking lots.  
There are mostly open spaces (parking lots) near crossing #2. The surroundings of the other crossings have a combination of a few low-rise buildings, open spaces, and trees. Crossing #3 is placed where the train track crosses a four-lane vehicle road. Crossing #4 has the most buildings and trees in its surroundings. Crossing #5 is somewhat similar to a combination of crossings #2 and #3 due to a small LoS window, which was due to the buildings before the crossing and a mixture of open spaces and trees after the crossing. Hence, the potential shadowing and fading sources were mostly buildings, trees, and cars. Based on the surroundings, we considered crossings #1, #2, and #5 to simulate rural environments, #3 a rural-suburban environment, and #4 a suburban-urban environment.
## 4 Discussion
These could include changes in attention level, exhaustion, or impediment, as well as social aspects such as emotional reactions, interactions, and social cues encountered during BCI training, which should be taken into consideration. Non-physiological reasons include a lack of proficiency in operating the system or inadequate training procedures, which lead the participant to misunderstand the instructions and execute the experiment erroneously [71]. That is why it leads to high standard deviation values in both datasets. Consequently, the proposed method could only handle a few cases in \"BCI illiteracy\" and further research is required to tackle the phenomenon. Thirdly, the classification of feet vs. hand imagery is more discriminative than left vs. right-hand imagery as shown in both datasets, although it is more noticeable in BCI competition dataset IIa (4-10% difference) than in self-acquired dataset (2-5% difference). This might be due to more discriminable EEG patterns generated by feet MI than those induced by the left or right hand. [PERSON] et al. [68] explained that feet MI would elicit both mu and/or beta ERD in the mid-central cortex and mu ERS on two hemispheres, specifically in the hand presentation area [72]. The so-called \"focal ERD/surround ERS\" phenomenon was found in [8]. Hence, the authors in the aforementioned studies reported a more significant variation in 10-12 Hz band power when comparing different limbs (hand vs. feet) than homologous ones. Finally, the FBCSP (without feature selection) could only work with data that is large enough, while DFBCSP could be effectively functional with small-sized data. It can clearly be seen that FBCSP efficiency is comparative to other methods (Tables 2-4) when it is applied to the self-acquired dataset, whose number of trials ranges from 220-250 per class, but downgrades to 10-30% if applied to a BCI competition IIa (72 trials per class) or single measurement of the self-acquired dataset (50 trials per class) (Figure 9). To the best of the author's knowledge, the bottom line of this outcome lies in the number of features and physiological differences between days of data recording. In a single measurement, FBCSP extracts all possible features, including redundant ones, while DFBCSP selectively chooses them; thus, it has a better performance than FBCSP. However, due to daily physiological changes of human nature, the EEG patterns when performing MI tasks may have changed as well leading to changes in required features for task discrimination. When including all data of each subject into one evaluation, redundant features may become informative ones, resulting in better FBCSP performance. Further research is needed to confirm this hypothesis. Yet, the FBCSP is obviously unable to have an optimal computation cost as DFBCSP. Hence, DFBCSP is more suitable as a practical BCI system in terms of performance and computation cost.
# A Virtual Network Resource Allocation Framework Based on SR-IOV
## 4 Basic Idea and Implementation
### Status Monitor Module
#### 4.4.2 Network Status Monitor Module  
Our goal is to assign VFs to network-intensive VMs. I/O factor \(io\_degree\) can only distinguish I/O-intensive VMs, but it is impossible to determine whether the VMs are disk I/O-intensive or network I/O-intensive. Therefore, in order to allocate VF accurately, we also need to determine the network state of the VM.  
The black box technology is used to determine the I/O density of VMs, that is, to obtain the status information of each VM through the virtualization management tool _Libvirt_ in Dom0. However, we cannot use black box technology to obtain the network information of VM. Because we use _Bonding Driver_ to bind VF and para-virtualized NIC together in DomU, and VF bypasses the intervention of VMM. Therefore, we can only get the data of DomU para-virtualized NIC in Dom0. We use gray-box technology to obtain the network status information of the VM and send the data of each VM to Dom0 through _virtio-serial_ communication.  
_Virtio-serial_ is a para-virtualized universal bus based on the _virtio_ framework. When using _virtio-serial_ to communicate between a VM and a host, there is no requirement for network bandwidth, we built two _virtio-serial_ channels for each VM when acquiring VM network data, namely the _virtio data channel_ and the _virtio control channel_. The daemon running in the VM asynchronously listens to the control channel. When the host sends an instruction to the VM, the VM completes the response. To save resources, the data channel is always closed, only when the control channel listens for the corresponding request will the data channel be opened, when the data is transferred will be closed again.  
The pseudo file system _/proc_ is an interface for Linux to dynamically manipulate kernel information. It is another important way for Linux kernel space to exchange data with user space outside of system calls. The _Network Status Monitor module_ reads the Linux kernel data (i.e., reads the file _/proc/net/dev_) to get the VM's network status accurately. The total flow of sending (or receiving) minus the total flow of sending (or receiving) last time is the total flow of sending (or receiving) during this sampling period. We define network factors that determine the density of VM networks:  
\[net\_degree=\varepsilon\times data\_avg+(1-\varepsilon)\times traffic\_freq \tag{4}\]  
where:  
\[data\_avg=(\sum_{0}^{num}traffic\_data)/num \tag{5}\] \[traffic\_freq=traffic\_num/num \tag{6}\]  
Among them, \(traffic\_data\) represents the total size of the transmitted and received data packets in one sampling period, \(num\) represents the total number of samples, \(traffic\_num\) denotes the number of samples of network transmission in all samples, \(data\_avg\) reflects the weighted average of the total traffic of the VM network data, \(traffic\_freq\) responding to the busyness of the network, parameters \(\varepsilon\) are used in Equation (4) to balance the weight between the two.