esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Framework
### TDMA/CA MAC Protocol
If the traffic production rate is known by this entity, it can allocate longer time slots for colors having high traffic. However, it is not sufficient to increase the duration of the sender time slots, as the traffic is routed to other nodes with other colors. The central entity has to take into account the routes used by the routing protocol in order to optimize the color allocation. * If the traffic production rate is not known by the central entity, it is possible to insert a CSMA/CA period between the colored period and the inactivity period. All the nodes are active during this period, and they are all able to send. This period can be used as a buffer for the traffic that exceeds the duration of the colored time slots, and it can also be used to accommodate non-colored nodes to join the network. This approach has been studied in [26, 27]. * SERENA is based on node coloring. Node coloring has the advantage to allow a node to transmit any type of traffic (unicast or broadcast) in its slots. Hence, a node can optimize its transmissions in the slots associated with its color, unlike link coloring where the slots granted to a link cannot be used for another traffic sent by the same node. If the node has no more traffic to send, it can sleep, as well as its one-hop neighbors after having detected a silent period. However, taking into account traffic heterogeneity has an impact on the delay of the mechanism. This issue is a perspective of our work. Some simulation results are reported in Section 4.6.
## 2 The Proposed Video QoE Metric for Dynamic Adaptive Streaming Services  
Figure 1 represents the framework of the QoE assessment model described in this paper. In the proposed model, firstly, 3D CNN and LSTM techniques are combined to extract deep spatial-temporal features to represent the content characteristics of a video. Next, factors such as video bitrate, video quality fluctuation, re-buffering and initial buffer, which distort the quality of a video, are extracted based on the DASH standard. Finally, the extracted features are combined to generate the input feature parameters vector, and the ridge regression method is used to map a model between the input feature parameters vector and the MOS value for predicting a user's QoE. The detailed process for developing the model is discussed below.
# On Mathematical Modelling of Automated Coverage Optimization in Wireless 5G and beyond Deployments
## 1 Introduction
Table 1 shows the terminology used in the remaining part of this paper adapted to the terms used in the literature. Automated wireless network design leads to a model formulated as Set Covering Problem (SCP) that belongs to the \(\mathcal{NP}\)-complete class of problems [11]. Whereas the SCP model was more of a mathematical puzzle trying to find the smallest number of subsets, whose union covers the whole set of elements (the universe), its extensions encapsulate the essence of design in efficiently allocating facilities across geographic space. These extensions commonly deal with aspects such as location, facility type, thresholds, capacities and others. For example, the Capacitated Location Set Covering Problem with Equal Assignment (CLSCP-EA) [12], which is concerned with capacity consideration, is assigning capacities equally. It works as follows: let us consider three service centres which cover one customer location, in that case, the customer location capacities are distributed equally among these three service centres. Currently, the extensions of SCP are not usable for modern telecommunication networks since they are not taking into account the important aspects related to the network capacity, i.e., (i) direct assignment of customer location capacities straight to the service centre (in the wireless networks, this task is equal to assigning users throughput demands to particular gNodeB node); and (ii) considering the network-area capacity requirements including the considerations of the existing services (here, it means to keep the existing gNodeB nodes in the process of optimizing the network coverage, for further information see Section 1.1). \begin{table}
\begin{tabular}{l l} \hline \hline
**Mathematical Terminology** & **Wireless Networks Terminology** \\ \hline Service centre & gNodeB node \\ \hline Customer location & a location to cover \\ \hline Capacity & throughput that is requested by sum of user requirements in a given location to cover \\ \hline Existing service & gNodeB that already exists in the area to cover and should remain after the reconfiguration or deployment phase \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mapping mathematical terminology to communication networks terminology. Figure 2: Sophisticated network planning.
## 3 The Proposed Method
### Framework Architecture
#### 3.1.2 Detector Module  
Using deep-learning techniques, the detector module detects intended objects and provides information about their classes and locations accordingly. To achieve this capability, the detector module needs to be trained on an annotated dataset consisting of adequate images of the desired classes. As high speed and accuracy are necessary in such real-time applications, the YOLOv4 (You Only Look Once, version 4) algorithm [25] was selected as the frameworks detector module. YOLOv4 is a significant update in the YOLO series. It employs a convolutional neural network (CNN) optimized for speed and accuracy. YOLOv4 introduces several improvements over its predecessors, including better utilization of GPU capabilities for enhanced performance. It uses features like Weighted-Residual Connections, Cross-Stage Partial connections, and a more efficient backbone, which contributes to its improved object detection capabilities. These technical advancements make YOLOv4 particularly suited for tasks requiring fast and precise image analysis, such as industrial quality control [25]. YOLOv4 is a one-stage object detection algorithm that, unlike the two-stage algorithms (e.g., R-CNN [30] or Fast R-CNN [31]), is suitable for real-time applications [32]. The selection of YOLOv4 in this study, despite other potentially more complex models, is driven by its real-time detection capabilities, aligning with our study's goal to demonstrate  
Figure 2: Framework architecture.  
the feasibility of using computer vision for detecting missing welds, as a practical proof of concept. The choice was also influenced by YOLOv4's proven effectiveness in real-time object detection, its compatibility with our industrial partner's IT environment, and the potential for future enhancements by the partner's teams. In our proposed framework, the corresponding Python wrapper for C-based Darknet, which is a framework for developing deep neural networks in the C programming language, is used to Integrate the Darknet implementation into our Python-based Framework.
## 3 Methods
### Improved A* Algorithm
#### 3.1.4 Simulation Experiment  
In order to test the effectiveness of the improved A* algorithm, two scenarios (with a map size of 100 \(\times\) 100) are set up to carry out simulation experiments via MATLAB. We compared traditional A*, extended A* [30], and improved A* in this paper. The results are shown in Figure 5, where the green rectangle indicates the start point and the red circle indicates the target point.  
Table 1 shows the comparison of the number of nodes, planning time, and path length results of the three algorithms in the two experimental environments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Algorithm** & **Number of Nodes** & **Planning Time (ms)** & **Path Length** \\ \hline \multirow{3}{*}{Scenario 1} & A* & 172 & 213.915 & 159.682 \\  & Extended A* & 166 & 193.178 & 154.424 \\  & Improved A* & 143 & 186.308 & 149.731 \\ \hline \multirow{3}{*}{Scenario 2} & A* & 227 & 562.713 & 231.812 \\  & Extended A* & 209 & 540.341 & 228.453 \\ \cline{1-1}  & Improved A* & 189 & 494.104 & 217.036 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of simulation results for three environments.  
Figure 4: Results of Clamped-B spline fitting with different number of times.  
Figure 3: Three-point non-colinear.  
Table 1 shows that, in Scenario 1, the improved A* algorithm, compared to the A* and the extended A*, resulted in a decrease of 16.86% and 13.85% in the number of path nodes, a decrease of 12.91% and 3.56% in the algorithm planning time, and a decrease of 6.23% and 3.04% in the path length, respectively. In Scenario 2, the improved A* algorithm showed decreases of 16.74% and 9.57% in the number of nodes, 12.19% and 8.55% in the algorithm planning time, and 6.37% and 4.98% in the path length, respectively, when compared to the A* and the expanded A*. These results indicate that in different obstacle environments, compared to the A* and the extended A*, the improved A* algorithm significantly reduces the number of nodes and path length and has superior performance in path search.
# An Autoencoder-Based Task-Oriented Semantic Communication System for M2M Communication
## 3 Proposed Autoencoder-Based Image Transmission System
### Communication Framework  
Polar codes, a significant development in coding theory, are denoted by the notation \(\mathcal{PC}(N,K)\), where \(N\) and \(K\) respectively represent the block length and the number of message bits. The concept of channel polarization, introduced by [PERSON] [34], involves the transformation of a physical channel into highly reliable and highly unreliable virtual channels as the code length grows toward infinity. This technique has been proposed as an effective method to improve the reliability and efficiency of communication systems. Several studies, such as [35; 36; 37], have demonstrated the efficacy of polar codes in achieving high throughput or low latency, making them a promising solution for various communication applications. Polar codes can leverage the identification of the most reliable channels by using Bhattacharya parameters [34] or Gaussian approximation [38]. Subsequently, these favorable positions can be utilized for embedding the information bits.  
The specification of the channel coding scheme used in this paper is elaborated in Table 2, outlining the intricate details of the selected scheme. Polar codes under successive cancellation algorithms are selected as our preferred channel coding scheme. A polar code of size \(N=2048\) with rate \(R=0.78\) is chosen to validate the proposed SC system. Like the methodology employed in [28], the selected polar code is optimized for an SNR of 2.5 dB, where the codewords are modulated utilizing BPSK across an AWGN channel. Finally, a quantization scheme involving 5 bits is employed to encode the pixel values, allowing the output image layer to be transmitted in a single packet. At the receiver end, the noisy LLRs are processed by the polar decoder, which computes the estimated codewords. The BER and FER of the selected polar code are depicted in Figure 6. Obviously, as the SNR grows, the error rate decreases. Based on this observation, it can be inferred that the BER becomes practically negligible in our specific application at an SNR of 4 dB or higher since the robustness of the latent vector can tolerate the BER values of more than \(10^{-3}\). This can be further explained in Figure 7 where PSNR achieves its maximum value above 4 dB.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Channel en/decoder & Polar Code \\ Decoding algorithm & Successive-Cancellation \\ Number of information bits (\(N\)) & 1600 \\ Number of codeword bits (\(K\)) & 2048 \\ Code Rate (\(R\)) & 0.78 \\ Image resolution & 10 \(\times\) 32 \\ Number of bits used for quantization (\(Q\)) & 5 \\ Modulation scheme & BPSK \\ Number of bits per symbol & 2 \\ Demapping method & LLRs \\ Channel Type & AWGN \\ \hline \hline \end{tabular}
\end{table}
Table 2: Channel encoding/decoding setup.
## 3 Results
### Phase 2--Behavioural Observation of Alpacas from Dusk until Dawn  
#### 3.2.1 Common Behaviours of Alpacas over 3-Day Observation Period  
When investigating the differences between alpacas for the proportion of time (minutes) spent in each state behaviour, on each day, the alpacas behaved similarly with only the differences observed in grazing behaviour on Day 1 (\(\chi^{2}(1)=26\), \(p<0.001\)); and resting behaviour on Day 1 (\(\chi^{2}(1)=35\), \(p<0.001\), Day 2 (\(\chi^{2}(1)=23\), \(p<0.001\) and Day 3 (\(\chi^{2}(1)=6\), \(p<0.05\)). There was also little difference between  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Behaviour** & \multicolumn{5}{c}{**Time Period (h)**} \\ \hline State Behaviours (\%) & 07:00–07:30 & 09:00–09:30 & 12:00–12:30 & 15:00–15:30 & 18:00–18:30 \\ \hline Grazing & 33.1 & 46.2 & 47.8 & 66.8 & 79.2 \\ Resting & 43 & 27 & 20.9 & 14.8 & 0.5 \\ Sleeping & 1.2 & 4.2 & 2.5 & 2.8 & 0 \\ Walking & 15.8 & 9.5 & 17.1 & 11.7 & 13.7 \\ Standing & 4.7 & 8.9 & 7.9 & 2.3 & 1.5 \\ Alert & 1.6 & 4.2 & 3.7 & 1.6 & 5 \\ Running & 0.6 & 0.1 & 0.1 & 0 & 0.1 \\ \hline Event behaviours & \multicolumn{5}{c}{} \\ (average frequency) & \multicolumn{5}{c}{} \\ \hline Sniffing lamb & 2.1 & 1.8 & 10.7 & 0.2 & 1.9 \\ Sniffing ewe & 0.5 & 0 & 1 & 0.3 & 0.7 \\ Sniffing alpaca & 0.1 & 0.1 & 0 & 0.1 \\ Scattching & 9.9 & 10.7 & 13.1 & 9.4 & 9.3 \\ Defecating & 0 & 0.3 & 0.3 & 0.7 & 0.1 \\ Rolling & 0.1 & 0.1 & 0.7 & 0.1 & 0 \\ Kicking & 0.1 & 0 & 0.3 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average time spent on state behaviours (%) and average frequency (number) of event behaviours by alpacas (\(n=2\)) during the 5 different time periods (hours) across the 10 observation days. ‘Drinking’ behaviour was excluded due to low occurrence. Each time period represents 30-min of behavioural observation (shown here as a percentage of time spent performing behaviour within the 30-min window). Refer to Methods Section 2.3 for definition of behaviours.  
testing days for each alpaca in the proportion of time spent in each state behaviour. The only difference between testing days observed was for the relative proportion of time spent in resting behaviour for each alpaca (Day 1 = 41 vs. 25%, Day 2 = 27 vs. 36%, Day 3 = 31 vs. 42%, \(\chi^{2}(2)=40\), \(p<0.001\))  
The most frequent event behaviour again displayed by both alpacas over the 16.7 h observation period was scratching (Table 4). The same alpaca as in Phase 1 was observed to scratch more frequently than the other (226 vs. 356 events, \(\chi^{2}(1)=13.02\), \(p<0.0005\)). The number of sniffing events shown by each alpaca was significantly greater towards older lambs than ewes for one alpaca (14 vs. 2 events, \(\chi^{2}(1)=9\), \(p<0.003\)) only but not for the other alpaca (18 vs. 16 events, \(\chi^{2}(1)=0.118\), \(p=0.73\)). There was no statistically significant difference for the frequency of events between each alpaca in other event behaviours including 'defecating', 'rolling', 'kicking' and'sniffing alpaca'.
# Systematic Review of Discrete Event Simulation in Healthcare and Statistics Distributions
## 4 Discussion
### Application Areas
#### 4.2.7 Cross-Departmental Integration  
A growing area of DES research focuses on modeling interactions between departments to enhance system-wide efficiency. [PERSON] et al. [PERSON] et al. (2016) demonstrated that integrated DES models encompassing multiple departments achieved 15% greater overall efficiency, improved resource allocation, and provided more realistic predictions of hospital-wide impacts. Their findings underscore the importance of cross-departmental simulation in capturing interdependencies and enhancing the effectiveness of healthcare operations.
## 3 Materials and Methods
### Datasets
#### 3.4.1 Open Datasets  
As individual open datasets have different annotation label categories, we extracted instances containing vehicle- and human-related objects. All instances were resized or cropped to 640 \(\times\) 640 images.  
* Visible Light Datasets: Considering the infrared target application, all visible light images were converted to greyscale via post-processing. For pre-training of the base network, MS COCO [8] was used. Image instances which contained small-sized objects (<30 \(\times\) 30 pixels) in the vehicle and human (person) categories were chosen. Additionally, the VEDAI [48] and DOTA [49] datasets are used in training. Both datasets are based on aerial imagery containing small objects. Images containing vehicle categories were selected for both training and benchmarks. The size of the target objects were concentrated from 12 \(\times\) 12 pixels to 25 \(\times\) 25 pixels.
* Infrared datasets: The Teledyne FLIR Free ADAS Thermal Dataset v2 [47] provides pairs of thermal and visible spectrum images for object detection. Among the instances, images containing people, bikes, cars, buses, and trucks were extracted. The infrared images were additionally annotated as \"infrared, real\", and RGB pairs were annotated as \"visible light, real\" for domain adaptation training.
# In-Situ Seawater Gamma Spectrometry with LaBr\({}_{3}\) Detector at a Nuclear Power Plant Outlet
## 3 Results
### In-Situ Continuous Observation Results  
From April to August 2018, the accumulative seawater gamma spectrum was collected using the LaBr\({}_{3}\) underwater detector mounted on a buoy at the discharging outlet of Ningde NPP. During the monitoring process, the spectrum data was automatically saved every 5 min to ensure the continuity and integrity of the data. Afterward, self-stabilization correction was performed, and then the energy spectrum was superimposed to obtain the accumulated spectrum under long-term measurement, which was used as the spectrum for data analysis (Figure 5).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Radionuclide**} & \multicolumn{2}{c}{\(\gamma\)**-ray**} & \multicolumn{2}{c}{**Emission**} & \multirow{2}{*}{**MDAC in 120 h**} & \multirow{2}{*}{**MDAC in 1 h**} \\  & **Energy** & **Probability** & & **(Bq L\({}^{-1}\))** & \\  & **(keV)** & **(\%)** & & **(Bq L\({}^{-1}\))** & \\ \hline \({}^{54}\)Mn & 834.848 & 0.09998 & 0.150 & 1.63 \\ \({}^{51}\)Cr & 320.0824 & 0.0991 & 0.586 & 6.39 \\ \({}^{137}\)Cs & 661.657 & 0.851 & 0.101 & 1.10 \\ \({}^{124}\)Sb & 602.726 & 0.9779 & 0.144 & 1.57 \\  & 1690.971 & 0.4757 & 0.018 & 0.20 \\ \({}^{134}\)Cs & 604.721 & 0.9762 & 0.092 & 1.00 \\ \({}^{134}\)Cs & 795.864 & 0.8546 & 0.157 & 1.71 \\ \({}^{110m}\)Ag & 657.76 & 0.9561 & 0.092 & 1.00 \\  & 884.678 & 0.7496 & 0.196 & 2.14 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Minimum detectable activity concentrations of targeting radionuclides.  
Figure 4: Spectrums before (left) and after (right) self-stabilizing correction.  
The characteristic peaks found in the energy spectrum are mainly 351 keV and 609 keV, besides the characteristic peak of lanthanum bromide itself. There are a few possible sources for the 609 keV peak: the 609 keV characteristic \(\gamma\)-ray of the natural radionuclide \({}^{214}\)Bi, which is one of the daughters of \({}^{222}\)Rn and is affected by precipitation or the sea flows [22; 23; 24], or artificial radionuclides \({}^{124}\)Sb (602.7 keV) or \({}^{134}\)Cs (604.7 keV), which are two common radionuclides in liquid effluents from NPPs.
# Q-Pandora Unboxed: Characterizing Resilience of Quantum Error Correction Codes Under Biased Noise
## 4 Examining Noise in Quantum Computing
### Experimental Framework  
We examined two QECCs, the rotated and unrotated surface codes, under the noise types and models previously outlined. Table 1 showcases QECC features, including distance, qubit count, and gates used. As distance and rounds grow, so do qubit and gate overheads. Using the equation \(rounds=distance\times 3\), we visualize computation as a 3D structure: one \(d\times d\) lattice per round, for a total height of \(3d\). All displayed logical error rates are per round, decoded using the MWPM approach [35, 36].  
In this study, we conduct our experiments using STIM [31], a high-speed simulator for quantum stabilizer circuits. STIM leverages a stabilizer tableau representation, akin to the CHP simulator [45], but also exhibits significant enhancements for performance and accuracy. As corroborated by the paper [17], STIM offers an unparalleled level of fidelity in emulating the operation of QECCs on actual quantum hardware. We configured STIM simulations to evaluate repetition and surface codes under different distances, rounds, noise types, noise models, and physical error rates. The noise models included depolarizing, gate, readout, and reset errors with controlled probabilities to capture diverse error scenarios. Logical error rates were extracted from the simulator outputs across the sampled physical error rates. These values were used to generate plots relating logical and physical error rates for each code and noise configuration. By analyzing the logical error rate curves, we identified accuracy thresholds where the QECCs begin failing. Comparing thresholds and resource overheads allowed for assessing the relative performance and limitations of the codes. This simulation-based workflow enabled a comprehensive evaluation of the noise resilience and optimal implementation of QEC codes to match target logical error rates and hardware constraints.  
Given the present constraints and limitations in the number of qubits of real quantum hardware (freely accessible ones), the execution of QECCs directly on these platforms is not feasible. Hence, STIM proves to be the best tool for our research.
## 4 Experiment and Results
### Results
#### 4.2.1 Time Consumption  
Table 2 is the time comparison of different methods. We divided the reconstruction time into two parts: the time for calculation of camera parameters and the time of 3D reconstruction. As can be seen from the table, camera parameters were calculated for deep learning methods based on COLMAP, so the time for the first part was always 61 min. In terms of 3D reconstruction, we set up two sets of resolution experiments to compare the time of 3D reconstruction. When the resolution of the input image was 1184 \(\times\) 800 (pixel), the time consumption of AA-RMVSNet and \(D^{2}\)HC-MVSNet was approximately 10 times and 7.8 times that of CasMVSNet, respectively. When the resolution of the input image increased to 2160 \(\times\) 1440 (pixel), the time consumption of CasMVSNet was also significantly lower than the other methods. As indicated in the table, the 3D reconstruction time of AA-RMVSNet and \(D^{2}\)HC-MVSNet was about 9.6 times and 8.6 times that of CasMVSNet, respectively. The CasMVSNet method took the shortest time and had the highest efficiency.  
The reason for the time difference is that both \(D^{2}\)HC-MVSNet and AA-RMVSNet use a recurrent neural network to regularize the cost map at each depth in the cost aggregation part. Compared with \(D^{2}\)HC-MVSNet, AA-RMVSNet adds an Inter-view AA module to the feature extraction part for multi-scale feature fusion and adds the Inter-view AA module before aggregating cost volume, which takes the longest time. The CasMVSNet model uses a cascading approach to calculate the pixel depth from coarse to fine. The depth range is roughly divided at the initial stage, and the calculation result is then discretized into a depth range for the next stage. Given that the sum of its depth intervals at each stage is much smaller than the depth interval values of the above two methods, and CasMVSNet uses a 3D CNN method that is faster than the recurrent neural network for cost aggregation, the method is the shortest and most efficient.