esa-sceva/satcom-chunk-collection huggingface dataset:
# AI-Assisted Multi-Operator RAN Sharing for Energy-Efficient Networks
## 6 Implementation, Results, and Analysis
### System Setup, Simulation Parameters, and Assumptions  
For the network deployment, we considered an urban deployment scenario consisting of seven three-sector sites, as seen in Figure 9. We consider three operators operating simultaneously in that area, each with a peak traffic demand of 350 Mbps/km\({}^{2}\)[21]. The intersite distance was set to 1200 m, and the observation window (as defined in Section 4.1) during which we calculated the energy consumption was 160 ms. To run the simulations, we used an event-based simulator where an event corresponded to a change in the traffic demand and the network scenario. The performance of the proposed approach  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Band**} & \multirow{2}{*}{\begin{tabular}{c} **Frequency** \\ **(MHz)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **MNO** \\ **Bandwidth** \\ **(MHz)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **TowerCo** \\ **Bandwidth** \\ **(MHz)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Type of** \\ **Band** \\ **(MHz)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Possible** \\ **Antenna** \\ **Configurations** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Possible Sleep** \\ **Levels** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Sleep Deltas** \\ **Sleep Deltas** \\ \end{tabular} } \\ \hline
1 & 700 & 10 & 30 & Coverage & 4,2 & SM1, SM2, SM3 & 0.84, 0.69, 0.50 \\
2 & 2100 & 20 & 60 & Capacity & 4,2 & SM1, SM2, SM3 & 0.84, 0.69, 0.50 \\
3 & 3500 & 100 & 300 & Capacity & 64, 32, 16 & SM1, SM2, SM3 & 0.84, 0.69, 0.50 \\ \hline \end{tabular}
\end{table}
Table 4: MNO resource allocation.  
Figure 16: Process flow.  
was measured in terms of the two network KPIs considered in the reward function (see Section 5.4). In the following subsections, we look at some assumptions from an operator and TowerCo's perspective along with the baseline used to compare our results.
# Proving Properties of [PERSON]'s Algorithm for Mutual Exclusion of N Processes
## 4 Conclusions  
[PERSON]'s mutual exclusion algorithm for two processes (Dekker-2) [1; 2; 3] is the first well-known software-based solution that has triggered the development of subsequent algorithms for \(N\geq 2\) processes. In [22], Dekker-2 and some of its variants were formally modelled and verified by model checking under atomic and non-atomic registers [6; 7], as well as in the context of a standard tournament tree-based organization [24; 26]. The adopted approach is based on timed automata [17] and the Uppaal toolbox [18; 19; 20]. It is intuitive because it supports the graphical specification of processes as automata in a high-level modeling language. It is powerful for the exhaustive verification, assisted by Timed Computational Tree Logic (TCTL) queries [18], of concurrent and timed systems. In [1], [PERSON] proposed a new and fair mutual exclusion algorithm for any number of processes (Dekker-N). The author analyzed the algorithm using informal reasoning. To the best of our knowledge, no previous formal modeling and verification of the algorithm has been reported in the literature. This paper applied our Uppaal approach for a thorough analysis of Dekker-N. It emerged that Dekker-N is a fully correct and fair mutual exclusion algorithm when atomic registers are used. A competing process can suffer from an overtaking bound of \(N-1\). Dekker-N was then adapted to work with non-atomic registers where it was confirmed to be correct only when \(N=2\). For \(N>2\), Dekker-N loses its fundamental mutual exclusion property. However, as an original contribution of this paper, the adopted approach was capable of showing that by fencing, with the help of some physical mechanism, just one single common variable and thus forcing it to have atomic read/write operations, Dekker-N turns out to be correct also with non-atomic registers.  
A unique feature of our Uppaal approach is its capability of enabling a careful analysis of a general model as Dekker-N when timing is used in combination with concurrency and nondeterminism.  
The continuation of the research will be geared to the following points. First, continue experimenting with the approach and non-atomic registers in other mutual exclusion algorithms for any number of processes, e.g., in the context of the tournament tree organization [16; 22; 23; 24]. Second, compare Dekker-N with similar algorithms also based on a binary tree tournament approach. Third, implement the Dekker-N model and similar algorithms using the Theatre actor system [16; 30] to study the model with non-atomic registers and higher values of \(N\), using stochastic behavior and simulation.  
Conceptualization, L.N. and F.C; methodology, L.N. and F.C.; validation, L.N. and F.C.; formal analysis, L.N. and F.C.; writing-original draft preparation, L.N.; writing-review and editing, L.N. and F.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
All data are contained in the paper.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Approval by End Users  
All the users with disabilities involved in the pilot testing were interviewed. Such interviews showed how well ElGo was accepted by them They really enjoyed ElGo, mostly because they never had the possibility to play at the same level with peers and because they saw a very impressive probability to save a goal, i.e., something that was really impossible before. They reported positive feedback about the usability of the system and the flexibility of user controllers. Moreover, they had very enthusiastic comments about ElGo and they would not stop to use it even after hours of playing. We believe that, even though ElGo is considered a piece of AT, it is actually perceived as a game, and thus a pleasant object to use, as opposed to a wheelchair or other aids.  
All the users without disabilities were asked for feedback, using a questionnaire in the first pilot testing session and interviews in the second one. ElGo was readily also accepted by them since playing with/against it was really challenging and engaging. We also noticed another important thing during the open tests: kids without any impairment wanted to try ElGo as the goalkeeper, probably because they perceived it as a sort of videogame brought to reality. We believe this is very important for social inclusion, since it is unusual that people without disabilities would like to use pieces of AT; this provides a situation in which the person with disabilities can feel the same as their non-disabled peers.
## 6 Conclusions  
This paper is aimed at investigating the feasibility of using the FRA technique to detect short circuit faults within induction motors through experimental measurements and statistical indicators. Various statistical coefficients are employed to quantify the comparison of the faulty and normal FRA signatures. Measurements conducted on two induction motors of different sizes reveal the feasibility of the FRA method to detect various short circuit faults within the stator winding. Results show that there is a minor variation in the measured frequency response due to turn-to-turn, turn-to-ground, and coil-to-ground SC faults. On the other hand, a substantial variation is observed due to coil-to-coil and phase-to-phase faults. To avoid misinterpretation of the FAR results, several statistical indicators including CC, SD, ASE, MSE, RMSE, DABS, Covariance, and MM are recommended to be calculated for the measured FRA signatures. Unique threshold limits for some indicators can be set to ease and standardize the FRA interpretation process. A further feasibility study is recommended to explore the impact of other induction motors' common faults on the FRA signature and statistical indicators. In addition, further research to enhance the robustness of results through providing statistical significance detailed analysis is recommended. The proposed indicators must also be tested on various induction motors of different sizes, models, and operating conditions.  
Conceptualization, S.M.A.-A. and M.F.M.Y; methodology, S.M.A.-A., M.F.M.Y., M.S.K., and A.A.A.; software, S.M.A.-A., M.F.M.Y., and A.A.S.; validation, S.M.A.-A. and M.F.M.Y.; investigation, S.M.A.-A., M.F.M.Y., and M.S.K.; resources, S.M.A.-A. and M.F.M.Y.; writing--original draft preparation, S.M.A.-A. and M.F.M.Y.; writing review and editing, M.F.M.Y., M.S.K., A.A.S., A.A.-S., M.I.M., and A.A.A.; Project administration: M.F.M.Y. and M.S.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Higher Education Malaysia under the Fundamental Research Grant Scheme Vot No. FRGS/1/2018/TK10/UTHM/02/10 and partially sponsored by Universiti Tun Hussein Onn Malaysia Publisher's Office via Publication Fund E15216 and The Islamic University, Najaf, Iraq.  
Not applicable.  
All involved in the content of the study.  
The data presented in this study are available in this article.  
**Acknowledgments:** The authors gratefully acknowledge the Post-Doctoral Fellowship Scheme under the Professional Development Research University Grant (05E68) from Universiti Teknologi Malaysia (UTM). The authors would like to thank [PERSON] from TNBC Malaysia for his assistance during the experiment. We would also like to thank all the subjects who volunteered to participate in this study.  
**Conflicts of Interest:** The authors declare there are no conflict of interest.
# An Effective Sensor Architecture for Full-Attitude Determination in the HERMES Nano-Satellites
## 3 Sensor Selection
### Accelerometers  
Accelerometers are used to measure the on-board non-gravitational accelerations, such as those generated by non-gravitational external perturbations and orbital control maneuvers. The selection of the Memsense MS3025 IMU guarantees the availability of a three-axis accelerometer. This sensor's measurements allow marginally improving the GNSS-based orbit determination accuracy and increasing a little the reliability of the on-board orbital propagation [7]. Indeed, the noise level is in the order of \(\sim\)1 ug at 1\(\sigma_{g}\), which is in the same order of the main non-gravitational external perturbations. However, it can be very useful if the proposed sensor architecture is applied to a platform with orbital control capabilities.  
The measured performance is reported in the specification matrix in Table 8. In this case also, the Allan variance method was used to perform the accelerometer's stochastic processes analysis, whose results are shown in Figure 13. The zero-rate outputs of the IMU's accelerometer were sampled at 25 Hz through the RS-422 serial interface. The IMU was configured with a full-scale range of 2 g, and the steady-state component temperature was at 38\({}^{\circ}\)C. The Allan variance data were sampled over a time span of 6 h.
## 2 Methods  
In this paper, we build an aircraft detection framework for multi-scale and arbitrary-oriented aircraft in remote sensing images, as illustrated in Figure 1. Our contributions consist of following major parts.  
1. We propose a rotated aircraft detection framework based on RetinaNet. The framework integrates multi-feature fusion and rotating anchors generation mechanism, which can efficiently detect arbitrary-oriented aircrafts and fit oriented bounding boxes.
2. Multi-feature fusion. Multi-scale features are extracted from a single resolution input image by feature pyramid network (FPN) with a top-down pathway and lateral connections, which includes high semantic features, as well as basis spatial detail features. In addition, in order to enrich the feature expression, Harris corner features are extracted from binary feature maps of different scales in feature pyramid layers and together fused into the feature map group.
3. Rotating anchors generation mechanism. To build a rotation-aware aircraft detector, we adopt five parameters (x, y, w, h, and \(\theta\)) to describe object bounding box (OBB), and design multiple rotating anchors with different aspect ratios, angles, and scales, which can fit OBBs well and provide accurate positions of arbitrary-oriented aircrafts.  
The overview framework of oriented aircraft detection is shown as Figure 1. First, the high semantic feature is extracted by ResNet-101, and the Harris corner feature is extracted from binary remote sensing images. In order to obtain sufficiently well expression of the aircrafts multi-scale characteristics, we use FPN to extract multi-levels of features that include high semantic features and low detail features (much information about location, scale and shapes). At the same time, multi-feature fusion also integrates with Harris corner, which can well describe the aircraft local salient feature and enriches the feature expression. Moreover, we design rotating anchor generation mechanism to improve the sensitivity of location and the fitting of OBBs. Finally, oriented aircraft detection is achieved by classification and regression. More details are provided in the following subsections.
## 2 Connection Options and Topology Optimization after Site Densification
### Obstacle Losses  
By careful link design and installation, any unwanted extra loss should be avoided, to preserve the highest possible fade margin for the link. Microwave and millimeter-wave links require clean  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Frequency Band (GHz)** & **13** & **15** & **18** & **23** & **28** & **32** & **38** & **58** & **60** & **80** & **90** & **100** \\ \hline \hline \(A_{\mathrm{sp}}\)**, atmospheric attenuation (dB/km)** & 0.02 & 0.03 & 0.07 & 0.2 & 0.1 & 0.09 & 0.13 & 13 & 15 & 0.36 & 0.4 & 0.45 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Atmospheric attenuation at different frequencies.  
Figure 12: Combined atmospheric and rain attenuation in dB/km as a function of frequency [12]. The figure is re-printed with the permission of ETSI.  
line-of-sight conditions that are checked in the planning phase of the links in two ways. The first method is to use digital maps, digital terrain, and clutter models in the link planning tool. The second method--not to be forgotten--involves site visits, where the LoS of the planned link is confirmed and photographed. It is strongly recommended to always document the clean LoS in an LoS report and to insert the confirmed LoS paths into the planning tool (green lines in Figure 3). With actual high-performing digital cameras, links of 50 km or even longer can be checked. Later, trees (Figure 13b), new buildings, cranes, electric cables, or chimneys may block the link. As a rule of thumb, at least, the first Fresnel zone of the link should be clean [32; 41]. However, in some urban environments, the obstacle loss cannot be avoided, e.g., when the link must operate behind window lamels or camouflage to reduce the visual impact of the antenna (Figure 13c). In such cases, the extra loss caused by the obstacle (Figure 13) should be taken into account in the link budget calculation (Equation (5)).
## 2 An Efficient Method and Its Acceleration
### An Efficient Method  
We rewrite Equation (5) as:  
\[\begin{split} X_{k+1}=X_{k}&(I+R_{k}(I+R_{k})(I-R_{ k}+R_{k}^{2})(I+R_{k}+R_{k}^{2})(I-R_{k}+R_{k}^{2}-R_{k}^{3}+R_{k}^{4})\\ &\times(I+R_{k}+R_{k}^{2}+R_{k}^{3}+R_{k}^{4})(I-R_{k}+R_{k}^{3}-R _{k}^{4}+R_{k}^{5}-R_{k}^{7}+R_{k}^{8})\\ &\times(I+R_{k}-R_{k}^{3}-R_{k}^{4}-R_{k}^{5}+R_{k}^{7}+R_{k}^{8}) )\end{split} \tag{7}\]Subsequently, Equation (7) results in the following formulation of Equation (5):  
\[X_{k+1}=X_{k}\left(I+(R_{k}+R_{k}^{2}+R_{k}^{3}+R_{k}^{4}+R_{k}^{5}+R_{k}^{6}) \left(I+R_{k}^{6}+R_{k}^{12}+R_{k}^{18}+R_{k}^{24}\right)\right) \tag{8}\]  
Now, we could deduce our final fast matrix iteration by simplifying Equation (8) more:  
\[X_{k+1}=X_{k}\left(I+(R_{k}+R_{k}^{2})(I+R_{k}^{2}+R_{k}^{4})\left(I+(R_{k}^{2} +R_{k}^{8})(R_{k}^{4}+R_{k}^{16})\right)\right) \tag{9}\]  
where only nine mmm are required. Therefore, the efficiency index of the proposed fast iterative method becomes:  
\[EI=31^{\frac{1}{9}}\approx 1.4645 \tag{10}\]  
The efficiency index \(1.4645\) of Equation (9) is higher than the efficiency index 1.4142 of Equation (3), higher than the efficiency index 1.4422 of Equation (4) and finally higher than the efficiency index 1.4592 of the 30 th order method proposed recently by [PERSON] _et al._[20].  
At this point, it would be useful to provide the following theorem regarding the convergence behavior of Equation (9).  
**Theorem 1**.: _Let \(A\in\mathbb{C}_{r}^{m\times n}\) be a given matrix of rank \(r\) and \(G\in\mathbb{C}_{s}^{n\times m}\) be a given matrix of rank \(0<s\leq r\), which satisfy \(\mathrm{rank}(GA)=\mathrm{rank}(G)\). Then, the sequence \(\{X_{k}\}_{k=0}^{k=\infty}\) generated by the iterative method Equation (9) converges to \(A_{R(G),N(G)}^{(2)}\) with \(31\)st-order if the initial approximation \(X_{0}=\alpha G\) satisfies:_  
\[\left\|F_{0}\right\|=\left\|AA_{T,S}^{(2)}-AX_{0}\right\|<1 \tag{11}\]  
Proof.: The proof of this theorem would be similar to the ones in [21]. Hence, we skip it over and just include the following error bound:  
\[\left\|E_{k+1}\right\|\leq\left\|A_{R(G),N(G)}^{(2)}\right\|\left\|A\right\|^ {31}\left\|E_{k}\right\|^{31} \tag{12}\]  
wherein \(E_{k}=A_{R(G),N(G)}^{(2)}-X_{k}\).  
The derived iterative method is very fast and effective, in contrast to the existing iterative Schulz-type methods of the same type. However, as was pointed out by [PERSON] _et al._[22], such iterative methods are slow at the beginning of the iterative process, and the real convergence rate cannot be observed. An idea to remedy this disadvantage is to apply a multiple root-finding algorithm on the matrix equation \(F(X)=X^{-1}-A=0\) and to try to accelerate the hyper-power method in its initial iterations. Such a discussion about a scaled version of the hyper-power method is the main aim of the next subsection.
## 7 Qualitative Evaluation and Open Discussion
### Privacy Preservation and Identity Protection  
The SSFXG model eradicates the need for a central authority to manage subscription services. The design allows NW subscribers to control their subscription information and determine when and how to access NW services. Furthermore, the NW subscriber and the in-service MNO anonymously handle the identity verification process during service provisioning requests. To prevent potential misuse of subscribers' identity information, access to the blockchain ledger containing this information is restricted to blockchain moderators (NW brokers and providers). This design feature addresses traceability concerns associated with NW subscribers.
# Local Dependency-Enhanced Graph Convolutional Network for Aspect-Based Sentiment Analysis
## 4 Experiment
### Ablation Study
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Twitter**} & \multicolumn{2}{c}{**Lap14**} & \multicolumn{2}{c}{**RestI4**} & \multicolumn{2}{c}{**RestI5**} & \multicolumn{2}{c}{**RestI6**} \\ \cline{2-10}  & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** \\ \hline IAN & 72.50 & 70.81 & 72.05 & 67.38 & 79.26 & 70.09 & 78.54 & 52.65 & 84.74 & 55.21 \\ AOA & 72.30 & 70.20 & 72.62 & 67.52 & 79.97 & 70.42 & 78.17 & 57.02 & 87.50 & 66.21 \\ BERT-SPC & 75.92 & 75.18 & 77.59 & 75.03 & 84.11 & 76.68 & 83.48 & 66.18 & 90.10 & 74.16 \\ AEN-BERT & 74.54 & 73.26 & 79.93 & 76.31 & 83.12 & 73.76 & 82.29 & 63.41 & 88.96 & 70.31 \\ ASGCN & 72.15 & 70.40 & 75.55 & 71.05 & 80.77 & 72.02 & 79.89 & 61.89 & 88.99 & 67.48 \\ DGEDT & **77.90** & 75.40 & 79.80 & 75.60 & 86.30 & 79.89 & 84.00 & 71.00 & 91.90 & 79.00 \\ BERT4 GCN & 74.73 & 73.76 & 77.49 & 73.01 & 84.75 & 77.11 & - & - & - & - \\ T-GCN & 76.45 & 75.25 & 80.88 & 77.03 & 86.16 & 79.95 & 85.26 & 71.69 & **92.32** & 77.29 \\ DualGCN & 77.40 & **76.02** & **81.80** & 78.10 & 87.13 & **81.16** & - & - & - & - \\ SSEGCN & 77.40 & **76.02** & 81.01 & 77.96 & **87.31** & 81.09 & - & - & - & - \\ Our model & 76.43 & 75.22 & 81.25 & **78.17** & 86.34 & **81.16** & **85.42** & **72.05** & 91.56 & **79.45** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results on five datasets. (Acc represents accuracy, F1 represents Macro-F1 score. The best results are displayed in bold, the second-best results are underlined. )Overall, our ablation study provides compelling evidence for the effectiveness of each module in the LDEGCN model, demonstrating their unique contributions to achieving superior performance in sentiment analysis tasks.
## 5 Numerical Evaluation Results
### Separated Peak Demands  
Figure 3c,d considers the more typical operational scenario when peak demands for the different operators are separated in time, e.g., due to different traffic and mobility patterns of the end users. We observe from Figure 3c that the SDN-based optimization with backhaul resource sharing among the two operators allocates up to 15 Mbps to the operator that currently experiences the peak demand (while 5 Mbps continue to serve the other operator); thus fully using the total available backhaul bitrate \(Z=20\) Mbps. In contrast, the benchmark without SDN does _not_ share backhaul capacity among operators. Accordingly, without SDN, operator 1 can only serve the peak demand that occurs from 10-20 s with its own 10 Mbps capacity; meanwhile, operator 2 uses only 5 Mbps of its 10 Mbps capacity and the other 5 Mbps are wasted.  
The SDN-based backhaul resource sharing reduces the queue build-up in the eNBs, as observed in Figure 3d compared to the benchmark without SDN, implying shorter latencies with SDN-based sharing. The slight variations between the optimization behaviors for the peak demands of operators 1 and 2 are due to the random variations of the actual demands around the prescribed mean Poisson traffic rates.
# Improved U2 Net-Based Surface Defect Detection Method for Blister Tablets
## 5 Experiments and Analysis of Results
### Evaluation Indicators  
In order to accurately evaluate the effectiveness of the model in the defect detection of blister tablets, this paper selects five indicators, including the average pixel accuracy (Accuracy,Acc), precision (Precision,P), recall (Recall,R), the combined evaluation of precision and recall (\(\beta=1\)), and the intersection ratio (IoU), for evaluating the overlap between predicted and actual results to evaluate the performance of the model.  
\[\text{Accuracy}= \frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{ FN}}, \tag{12}\] \[\text{Recall}= \frac{\text{TP}}{\text{TP}+\text{FN}},\] (13) \[\text{Precision}= \frac{\text{TP}}{\text{TP}+\text{FP}},\] (14) \[\text{F}_{\beta}- \text{Score}= \frac{(1+\beta^{2})\times\text{P}\times\text{R}}{\beta^{2}\times \text{P}+\text{R}},\] (15) \[\text{IoU}= \frac{|P\cap G|}{|P\cup G|}=\frac{\text{TP}}{\text{TP}+\text{FP} +\text{FN}}, \tag{16}\]  
In the formula, TP, TN, FP, and FN, respectively, represent the number of defect pixels correctly predicted by the model, the number of background pixels correctly predicted, the number of defect pixels incorrectly predicted, and the number of background pixels incorrectly predicted.