esa-sceva/satcom-chunk-collection huggingface dataset:
# Object/Scene Recognition Based on a Directional Pixel Voting Descriptor
## 5 Conclusions  
The importance of object detection under several applications, including surveillance, autonomous navigation, and augmented reality, cannot be overstated. While AI-based approaches like Convolutional Neural Networks (CNNs) have demonstrated high performance in object detection, there are limitations for unknown objects being detected. Feature-based methods such as SIFT, SURF, and ORB support unknown object detection but have limitations under visual variations. In response, this work presented a novel edge-based object/scene recognition technique. Our proposed directional pixel voting descriptor, based on image segments, represents a significant advancement in this domain. Experimental results demonstrated high performance compared with previous works in the current literature. In future work, improvements such as additional constraints for the descriptor previously laid down, non-maximal suppression methods (to avoid multiple matches for some features in the template and query), optimizations, and hardware accelerations will be implemented in order to improve processing speed and efficiency.  
Conceptualization, Investigation: A.A.-G. Validation and Writing--Original Draft: A.M.S. and J.A.d.J.O.-C. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflicts of interest.
# A Framework to Determine the Utilization of Vacant Taxis on HOV Lanes with the Optimal Deployment
## 3 Formulations
### Travel Mode Split and Path Choice  
In this paper it is assumed that the trips follow elastic demand, which can be described as:  
\[d_{rs}=\overline{d_{rs}}\cdot\exp(-\pi_{rs}\cdot\mu_{rs}),\ r\in R,s\in S \tag{20}\]where \(\pi_{rs}\) is the coefficient and \(\mu_{rs}\) is the generalized minimal cost between OD pair \(w\). The travelers follow the principles of a logit travel mode split and the UE principle. The logit formulation can be expressed as:  
\[d_{rs}^{m}=d_{rs}\cdot\frac{\exp[-\beta(\mu_{rs}^{m}-k_{rs}^{m})]}{\sum\limits_ {m\in M}\exp[-\beta(\mu_{rs}^{m}-k_{rs}^{m})]},\ r\in R,s\in S \tag{21}\]  
where \(\beta\) is the sensitive coefficient, \(\mu_{rs}^{m}\) is the minimal cost of travelers with mode \(m\) between OD pair \(rs\), and \(k_{rs}^{m}\) is the attraction of mode \(m\) between OD pair \(rs\). The UE principle for the travelers of the three modes is expressed as follows:  
\[f_{rs}^{m}=0,\ \text{if}\ c_{rs,p}^{m}\geq\mu_{rs}^{m},\ r\in R,s\in S,m\in M,p\in P \tag{22}\]  
\[f_{rs}^{m}>0,\ \text{if}\ c_{rs,p}^{m}=\mu_{rs}^{m},\ r\in R,s\in S,m\in M,p\in P \tag{23}\]  
Likewise, the UE principle for vacant taxis is described as:  
\[f_{sr}^{v}=0,\ \text{if}\ c_{sr,p_{v}}^{v}\geq\mu_{sr}^{v},\ r\in R,s\in S,p_{v} \in P_{v} \tag{24}\]  
\[f_{sr}^{v}>0,\ \text{if}\ c_{sr,p_{v}}^{v}=\mu_{sr}^{v},\ r\in R,s\in S,p_{v} \in P_{v} \tag{25}\]  
The UE principle states that each type of traveler always chooses the path with the minimal travel cost for this type of travel mode. No one chooses the paths with a travel cost larger than the minimum.
# Learning-Based Branching Acceleration for Unit Commitment with Few Training Samples
## 4 Branching Acceleration for Unit Commitment
### The Branching Acceleration for UC Framework
#### 4.3.2 Training Phase  
This study employs a data aggregation algorithm to iteratively update the training dataset for the enhanced pruning policy. During the iterative training, after solving any UC problem (training sample), the policy model will be promptly updated to correct erroneous decisions. This training method effectively reduces the size of the training dataset and the number of training samples required, not only improving training efficiency but also enhancing the practical application value of branching acceleration for UC. The detailed training process is outlined in Algorithm 2.  
For the first training iteration, an arbitrary randomized policy \(\kappa_{(1,0)}\) is used to solve the first UC problem. In the \(m\)-th training iteration, we apply the enhanced pruning policy \(\kappa_{(m,g-1)}\) to solve the \(g\)-th UC problem (a total of \(G\) UC problems as training samples, with a maximum of \(M\) training iterations). Firstly, the collected node features are input into the policy to obtain the corresponding decision actions (preserve or prune). Secondly, based on pre-collected labels, the node features with incorrect decision actions are added to the training dataset \(D^{\text{tra}}\). During the B&B tree search process, incorrect pruning has more severe consequences than incorrect branching. The optimal node represents the desired search path, and we expect that the enhanced pruning policy does not miss any of these nodes. Ideally, during the testing phase, the policy should preserve only the optimal nodes. Therefore, the features of all optimal nodes are added to \(D^{\text{tra}}\).  
Finally, the policy is updated to \(\kappa_{(m,g)}\) to solve the next UC problem. It is important to note that the frequency of updating the model in iterative training can be adjusted based on the specific characteristics of the problem. The update frequency of the training dataset remains unchanged.
## 1 Introduction  
The Taipei Metro is a metro system serving the Taipei metropolitan area, and encompasses an area of approximately 2500 km\({}^{2}\), which has a population of over seven million. Since its inception in 1996, the network of Taipei Metro has quickly expanded from 12 stations to 131, and from 10.5 km to 152.9 km [1]. In the same time frame, its daily ridership has also experienced a similar growth from forty thousand to over two million, making Taipei Metro the 22 nd busiest metro system in the world [2].  
Nevertheless, Taipei Metro has a reputation for being disorienting and confusing, especially to the infrequent passengers. According to a survey on its service quality conducted in 2020 by Taipei Rapid Transit Corporation, the control of passenger flow and the spatial disorientation are, respectively, rated as both the top and the second most in need of improvement by 38.7% and 33.5% of respondents [3]. As further detailed in the survey report, the most commonly identified cause of feeling disoriented is the complexity of the floor plan configuration and the lack of wayfinding guidance. Specifically, most of Taipei Metro stations have multiple underground levels that consist of a basement concourse and several boarding platforms (Figure 1). Inevitably, the vertical travel between different levels increases the difficulty for the passengers to find their way to either the exit gates or to specific destinations inside the station.  
To minimize the spatial disorientation in Taipei Metro stations, this paper firstly reviews the concept of spatial orientation and presents a case study of Zhongxiao Xinsheng Station using the methods of behavioral mapping and visibility analysis. Based on the data obtained from the case study, a mobile application that will enhance the wayfinding experience of passengers is proposed.
## 2 Materials and Methods
### Methodology
Consistent with those criteria, the Correlation coefficient values must be \(>\) 0.7 while rBIAS must be between \(-\)10 to 10, whereas positive and negative values indicate both over/underestimation, respectively. The seasonal assessment was conducted by dividing the year into three seasons, for instance, winter (October-February), pre-monsoon (March-June), and Monsoon (July-September). Furthermore, the Probability Density Function (PDF) method was used to evaluate the different precipitation intensities of respective GPPs. Those intensities were divided into seven classes (mm/day) for example, (1) 0-1; (2) 1-2; (3) 2-5; (4) 5-10; (5) 10-20; (6) 20-50; (7) \(>\)50 following World Meteorological Organization (WMO) standards with slight amendment owing to the local climate system. To assess the precipitation detection capabilities of GPPs in contrast to ground truth data, the categorical statistical metrics, including Probability of Detection (POD), False Alarm Ratio (FAR), and Critical Success Index (CSI), were applied [60; 61]. The calculation of these metrics is well defined by [62]. The POD is also termed the hit rate, which indicates the ratio of precipitation events correctly sensed by the GPPs to the number of actual precipitation incidents; its optimal values show mostly precipitation incidents were detected. The FAR shows the ratio of precipitation occurrences wrongly sensed as the number of actual precipitation events. The CSI is known as the function of POD and FAR, which defines the overall skills of precipitation incidents accurately sensed by GPPs. To distinguish precipitation and non-precipitation days, the threshold of 1 mm/day was used following [63]. These categorical skill indices were calculated using the following equations [64]:  
\[\text{POD}=\frac{H}{H+M} \tag{5}\]  
\[\text{FAR}=\frac{F}{H+F} \tag{6}\]\[\text{CSI}=\frac{H}{H+M+F} \tag{7}\]  
where \(H\) indicates the number of precipitation events correctly captured by respective products, \(M\) shows the number of precipitation occurrences missed (lost) by GPPs, and \(F\) represents the number of incidents falsely estimated as precipitation events by GPPs. The perfect value of POD and CSI is 1, whereas 0 is for the FAR.
## 3 Near Field Models of Reflectarray and Transmitarray Antennas
### Multi-Frequency Optimization of the Near Field
Nevertheless, after the direct layout optimization, in which the sizes of the two stacked rectangular patches are  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Frequency (GHz)**} & \multicolumn{4}{c}{**Polarization X**} & \multicolumn{4}{c}{**Polarization Y**} \\ \cline{2-9}  & **Ripple (dB)** & **1 dB (\%)** & **2 dB (\%)** & **3 dB (\%)** & **Ripple (dB)** & **1 dB (\%)** & **2 dB (\%)** & **3 dB (\%)** \\ \hline
26.5 & 5.34 & 42.45 & 63.35 & 82.03 & 5.92 & 30.40 & 58.80 & 78.97 \\
27.0 & 3.79 & 47.17 & 71.23 & 98.79 & 3.97 & 43.83 & 73.20 & 98.45 \\
27.5 & 2.53 & 43.63 & 93.75 & 100 & 2.53 & 48.57 & 89.96 & 100 \\
28.0 & 2.66 & 51.28 & 96.44 & 100 & 2.48 & 45.14 & 99.10 & 100 \\
28.5 directly optimized, the antenna is able to provide full coverage in the designated area with a maximum ripple lower than 2 dB. Table 4 gathers the performance of the optimized layout at seven equispaced frequencies covering the whole 5G NR n257 band. Now, a compliance of 100% is achieved in the whole band for a ripple of 2 dB. Indeed, the worst achieved ripple is 1.5 dB at 29.5 GHz for polarization \(X\). When compared with the results shown in Table 3, the worst ripple has been reduced more than 7.5 dB, and even the compliance for a ripple of 1 dB, although not considered a requirement, has improved from the lowest value of 26.4% for polarization \(X\) at 29.5 GHz to 76.7% for polarization \(X\) at 28.5 GHz. In the case of the 2 dB compliance columns, the minimum value before the direct layout optimization was 44.3%, which was improved to a 100% compliance after a successful wideband optimization. Figure 9: Representation of the normalized electric field magnitude for linear polarization \(X\) that complies with a ripple of 2 dB at 26.5 GHz (**left** column), 28 GHz (**middle** column), and 29.5 GHz (**right** column) generated by the layout obtained after the POS (**top** row) and after the direct layout optimization (DLO) with the GIA (**bottom** row). Coverage zone is represented by a solid red line and transition zone by a dashed red line. Near field is plotted in the NFCS.
# Collision Frequency and Energy Transfer Rate in e-He Scattering
## 2 Methods  
### Momentum-Transfer Cross-Sections via the Method of Phase Functions  
To accurately determine the collision frequency and energy transfer rate in particle interactions, it is essential to calculate the momentum transfer cross-section, which represents the average momentum transferred by a particle during a collision. This cross-section is calculated using the partial wave decomposition of scattered waves:  
\[Q_{T}^{ea}(k)=\frac{4\pi}{k^{2}}\sum_{l=0}^{\infty}(l+1)\sin^{2}(\delta_{l+1}- \delta_{l}). \tag{2}\]  
To perform this calculation, we employed the method of phase functions [27], also known as the variable phase approach [28]. This technique efficiently computes the phase shifts \(\delta_{l}\), by solving the first-degree phase equation, thereby circumventing the exhaustive wave function calculations typical of conventional approaches [29,30]. The interaction potential used in the phase equation is defined via the optical potential approach, as detailed in the following section.  
The phase equation is given as follows:  
\[\frac{d\delta_{l}(r)}{dr}=-\frac{1}{k}\bigg{(}\frac{2m_{e}}{\hbar^{2}}\bigg{)} V(r)[\cos(\delta_{l}(r))j_{l}(kr)-\sin(\delta_{l}(r))n_{l}(kr)]^{2}, \tag{3}\]  
and is solved with the boundary condition:  
\[\delta_{l}(0)=0, \tag{4}\]  
where \(\delta_{l}(r)\) is the phase function, \(V(r)\) is the interaction potential between an electron and an atom at a distance \(r\) from the atomic nucleus, \(j_{l}(kr)\) and \(n_{l}(kr)\) are the Riccati-Bessel functions, \(k\) is the wave number of the incoming electron, \(m_{e}\) is the electron mass, and \(\hbar\) is the reduced [PERSON]'s constant. The phase shift is defined as the asymptotic value of the phase function at large distances:  
\[\delta_{l}=\lim_{r\rightarrow\infty}\delta_{l}(r). \tag{5}\]
# Energy Efficiency and Coverage Trade-Off in 5G for Eco-Friendly and Sustainable Cellular Networks
## 2 Related Work  
A plethora of works have studied the switch-off approach. Various strategies for switching off BSs based on Universal Mobile Telecommunications System (UMTS) CNs during off-peak traffic conditions have been implemented [25; 26; 27]. [PERSON] et al. (2008) explored the likelihood of switching off some cells and BSs in the UMTS network during off-peak traffic durations, at the same time guaranteeing quality of service (QoS) constraints from the perspective of blocking probability and electromagnetic exposure limits. The researchers analyzed three kinds of scenarios: non-commercial, commercial, and hierarchical. It was shown that there was a 50% power saving in all the three scenarios utilizing BS switching. The previous work was extended by proposing a responsive network planning framework for BSs switching off/on involving uniform and hierarchical scenarios [26]. The results were extended in [27], and a realistic regular cell architecture was proposed wherein each of these configurations has a specific energy-saving ratio by turning three out of four or eight out of nine BSs off, thereby achieving an energy savings on the order of 25% to 30%. Furthermore, two energy-saving mechanisms have been proposed in [28]: (i) A greedy centralized algorithm, which is based on the concept that each BS determines which BS is fit to be switched off based on the traffic, and (ii) a decentralized algorithm involving an individual BS locally estimating its traffic load and making independent decisions as to whether it will be switched off. [PERSON] et al. (2010) adopted a dynamic switch on/off algorithm motivated by blocking probabilities. The BSs switched off are moderated based on the traffic variation with regards to a blocking probability constraint [29].  
[PERSON] et al. (2011) analyzed the minimal number of active BSs that will be utilized based on the balance between fixed and dynamic powers [30]. [PERSON] et al. (2012) proposed a new UMTS cellular access network energy-saving optimization model [31]. Using the average distance among BSs and UEs as a decision-making metric, [PERSON] et al. (2012) proposed an algorithm in which the BSs with maximum average distance will be switched off [32].  
This study proposes an approach to cooperation between the LTE and 5G technologies, which aims to strike an equilibrium between network performance and energy savings via switching off/on the 5G BSs influenced by instantaneous load traffic, ensuring the service coverage of the remaining mobile subscribers by the remaining active LTE BSs.
## 7 Conclusions  
The harshness of the sea environment is pushing the use of AUVs as a cost-effective and safe alternative to perform underwater missions. AUVs may collect large amounts of data from their onboard sensors that needs to be transferred to shore. A data muling solution outperforms the current long-range narrowband communications solutions.  
In this paper, we proposed an enhanced version of the UDMSim, which is a simulator for data muling oriented underwater communications that combines a more realistic AUV Motion and Localization simulator with ns-3. UDMSim matches the results obtained using a simple theoretical model and the lab experimental results when no localization errors are considered. When localization errors are taken into account, UDMSim is able to reproduce them and simulate the signal and connection losses that will occur in real environment, thus enabling the evaluation of underwater data muling oriented communications in more realistic conditions. Moreover, we have shown that UDMSim is now able to simulate the effect of the antenna radiation patterns on the SNR during the mission, thus helping to evaluate the design and relative placement of underwater antennas. Future work includes new AUV control laws for more accurate simulations, a relative positioning system to cope with mobile SUs, and the comparison of UDMSim results with experimental results obtained in real environment.  
Figure 18: Equivalent throughput for two DMUs in the _1 m apart_ scenario.  
**Author Contributions:** Conceptualization, F.B.T., B.M.F., R.C.; funding acquisition, F.B.T., B.M.F., N.A.C., J.C.A., R.C.; investigation, All authors; methodology, All authors; supervision, R.C.; All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is financed by the FCT--Fundacao para a Ciencia e a Tecnologia (Portuguese Foundation for Science and Technology) within project GROW (PTDC/EEI-COM/29466/2017). The first author would like to thank the support from the FCT--Fundacao para a Ciencia e a Tecnologia (Portuguese Foundation for Science and Technology) under the scholarship SFRH/BD/88080/2012.  
**Institutional Review Board Statement:** Not applicable.  
**Data Availability Statement:** All data has been present in main text.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
The increasing demand for modular and reusable steel structures has driven the development of demountable connections that preserve the integrity of structural components. This study investigated the structural performance of beam-to-column connections using clamp-based fastening systems, operating strictly within the elastic regime and targeting applications in temporary systems and industrial platforms. Two triangular steel frame configurations (180 mm and 260 mm), differing in clamp capacity and hole arrangement, were experimentally tested and numerically modeled to assess their influence on load-bearing capacity, displacements, and stress distribution. Experimental tests were conducted with controlled bolt pretension and progressive vertical loading, continuously monitoring displacements and applied forces. The finite element model (FEM), validated with high correlation (>97%) to the experimental data, confirmed that all configurations remained within the elastic domain. Results showed that increasing the number of damps significantly enhanced both stiffness and load capacity, with gains of up to 27.3% depending on the configuration, while reductions exhibited a nonlinear performance loss. Stress concentrations were observed in clamp contact regions without plasticization. Overall, clamp-based connections demonstrated efficient structural performance and alignment with design-for-deconstruction and circular economy principles, proving to be technically feasible for systems requiring reusability and adaptability.  
clamp-based connections; reusable steel structures; elastic behavior; finite element modeling; design for deconstruction +
Footnote †: journal: Applied sciences
# A Survey: Network Feature Measurement Based on Machine Learning
## 3 Network Measurement Based on ML Method
### Congestion Control  
The load of the communication network has a direct impact on the throughput of the network. If the throughput drops when the network load reaches a specific level, congestion develops. When congestion is severe, it will cause wasteful retransmissions, which will lower the communication subnet's effective throughput and eventually push the local or even all communication subnets into a state of deadlock, with an effective network throughput that is near zero. In the present situation, congestion detection in wireless sensor networks is a significant issue. Currently, several academics employ machine learning techniques to identify congestion.  
[PERSON] et al. [77] identified congestion in the transport-layer sink node using an NN-based congestion detection method. The output was the degree of congestion, while the input parameters were the number of participants, buffer occupancy, and traffic. Numerous simulation results demonstrate the scheme's ability to detect and better reflect the level of congestion in sensor networks. [PERSON] et al. [78] created a congestion detection classifier with three levels: low, middle, and high using an open-source SVM package and a sequence-to-minimum optimization approach. When the radial basis function is chosen as the kernel-function model, gamma and cost are crucial factors to consider. By modifying various factors, the ideal model training time can be achieved. Scholars generally agree that M5 decision trees are superior to general NN and that employing neural networks to predict network congestion is better than empirical models after analyzing various studies.