esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Adaptive Iterative Extended Kalman Filter Based on Variational Bayesian
## 2 Computational Procedure
### Algorithm 1  
The recursive operation of the improved adaptive iterative extended Kalman filter based on variational Bayesian (VBAIEKF) is depicted in the pseudocode in Algorithm 1.  
```
Input:\(\hat{X}_{t-1|t-1},P_{k-1|k-1},\hat{u}_{t-1|t-1},\hat{U}_{t-1|t-1},F,h\left( \hat{X}_{t}\right),\tilde{Z}_{t},\tilde{Q}_{t-1},a,b,\tau,\rho,N,\varepsilon\)
1 Time update: using (8)-(9)
2 Iterated variational measurement update:
3 Initialization:
4\(\hat{X}_{t|t}^{(0)}=\hat{X}_{t|t-1},P_{t|t}^{(0)}=\hat{P}_{t|t-1},\)
5 Using (14)-(15) and (17)-(18)
6 for\(i=0:N-1\)do
7 Update \(q^{(i+1)}\left(P_{t|t-1}\right)=IW\left(P_{t|t-1};\hat{t}_{t}^{(i+1)},\hat{T}_ {t}^{(i+1)}\right)\) given \(q^{(i)}\left(\hat{X}_{t}\right):\)
8\(H_{k}=\left.\frac{\partial h\left(\hat{X}_{t}\right)}{\partial x_{t}}\right|_ {\hat{X}_{t-1}=\hat{x}_{t|t-1}}\left(\hat{X}_{t}-\hat{x}_{t|t-1}\right)\)
9 Using (22), (24) and (25)
10 Update \(q^{(i+1)}(R_{t})=IW\left(R_{t};\hat{u}_{t}^{(i+1)},\hat{U}_{t}^{(i+1)}\right)\) given \(q^{(i)}\left(\hat{X}_{t}\right)\): Using (27), (29) and (30)
11 Update \(q^{(i+1)}\left(\hat{X}_{t}\right)=N\left(\hat{X}_{t};\hat{X}_{t|t}^{(i+1)},P_ {t|t}^{(i+1)}\right)\) given \(q^{(i+1)}\left(P_{t|t-1}\right)\) and \(q^{(i+1)}(R_{t})\) :
12 Using (31), (32), (36) and (38)-(40)
13 only if;
14 if\(\left\|\hat{X}_{t}^{(i+1)}-\hat{X}_{t}^{(i)}\right\|\leqslant\varepsilon\), stop iteration.then
15 end if
16
17 end for
18\(\hat{X}_{t|t}=\hat{X}_{t|t}^{(N)},P_{t|t}=P_{t|t}^{(N)},\hat{t}_{t|t}=\hat{t}_ {t}^{(N)},\hat{t}_{t|t}=\hat{T}_{t}^{(N)},\hat{u}_{t|t}=\hat{u}_{t}^{(N)},\hat{ U}_{t|t}=\hat{U}_{t}^{(N)}\) Output:\(\hat{X}_{t|t},\hat{t}_{t|t},\hat{t}_{t|t},\hat{u}_{t|t},\hat{U}_{t|t}\)
```  
**Algorithm 1** One time step of the proposed VBAIEKF
# Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks
## 4 Applications of ML in D2D Communications
### RA and Mode Selection (MS)  
The RA and MS processes in D2D communications show high complexity in highly dense networks. Leveraging ML methods for both RA and MS can promote more powerful flexibility to cope with network dynamics.The authors of [14] proposed a DL-based algorithm for transmission in D2D networks. They formulated a CNN that selects D2D linkages to transmit data, where 90% of the selected sub-optimal data are utilized to train the CNN based algorithm and the remaining 10% for validation. Their numerical results showed that they obtained 85-95% accuracies from the neural network. In [9], an autoencoder is trained using a supervised learning approach to pair underlay D2D transmitters to reuse the spectrum of CUs, where the optimal pairing in a dataset is obtained using the conventional Hungarian algorithm that minimizes the total cost of selection through combinatorial optimization formed as bipartite graph. The main idea is to use a Deep Learning (DL) approach that is capable of mapping the cost matrices of matching different D2 Ds-CUs to the corresponding optimal solutions defined by the assignment matrix obtained from the Hungarian algorithm with less complexity and time. In [8], the authors modeled the joint radio resource management (RRM)-MS problem as two stage online learning combinatorial MAB (CMAB). Their Combinatorial Bandit Learning for MS and  
Figure 3: Machine-learning (ML) applications in D2D networks.  
RA (CBMOS) algorithm achieves fast learning speed (132%) and higher performance (142%) for high channel dynamics. In [7], a RL-based latency controlled D2D connectivity (RL-LCDC) for indoor D2D was proposed. RL-LCDC intelligently finds the neighbors, determines the D2D connection, and adaptively manages the communication area for greatest network connectivity. Distributed Q-learning algorithm can automatically allocate the spectrum to control interference in D2D enabled multi-tier HetNets. The performance of the proposed RL based schemes was optimal compared to other techniques in terms of throughput, SE, signal to interference ratio (SINR), and network coverage. Additionally, clustering algorithms can be exploited for improving RA in single and multi-cell D2D network to obtain better SE and system performance. The authors in [15] utilized k-means clustering algorithm for improving RA in single cell D2D network yielding SE and system performance improvements.
## 5 Capacity Trends Analysis
### Actualized Factors
Optimal network scheduling should not only consider paths with fewer hops, but also consider many actualized factors, even including the local weather.
## 3 Approach and Analysis: Architecture and Methodology
### Methodology: Machine Learning-Based Approach
#### 3.3.1 Classification  
Classification of the dog sounds was carried out using the barking pattern. The barking pattern information observed for the different scenarios was used to classify the different types of dog sound. Figure 4 represents the reinforcement learning method for the prediction of the dog sounds. It comprises Environment and Agent.  
Figure 3: Proposed methodology for bark pattern classification.
# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 4 Solutions to the Issues Faced by RPL during Mobility
### Position-Based Solutions  
Sensors are attached to workers, goods, and equipment that require robust communication with the base station. Hence, during these scenarios, the position of nodes must be considered by routing protocols as the best routing decision based on their particular locations. The following proposed solutions consider the position information of mobile nodes for reliable routing and help in resolving the issue of lack of positioning information.
## 1 Introduction
Compared to state-of-the-art studies, this paper presents the following: (a) a high-precision detection method for CO\({}_{2}\) isotopes applied for deep-sea NGHs, (b) a linear optical structure combined with a multi-pass gas cell (MPGC) to overcome the challenges of a physically compact requirement for NGH exploration, (c) a high-precision temperature control system for the MPGC to improve the measurement precision of carbon isotopes, (d) a compact cylinder cell placed between the interband cascade laser (ICL) and MPGC to remove the interference from CO\({}_{2}\) in the environment and to improve the measurement accuracy, and (e) a prototype using a mid-infrared absorption spectroscope capable of CO\({}_{2}\) isotope detection in indoor experiments. In this study, a sensing system capable of highly precise measurements of the \({}^{13}\)CO\({}_{2}\)/\({}^{12}\)CO\({}_{2}\) isotope ratio of CO\({}_{2}\) gas separated from seawater was developed. The TDLAS technique is utilized to detect the carbon isotope absorption line which is located in the strong spectrum band at 4.2 \(\upmu\)m. The described sensor utilizes a mid-infrared ICL operating in continuous wave mode with low-temperature, long-path multi-reflection gas absorption chambers, and a mid-infrared MCT detector with high responsivity. An indoor experiment was conducted to verify the work performance and application feasibility of the proposed sensing system. The remainder of this paper is organized as follows: Section 2 introduces the carbon isotope detection principle and absorption line selection. Section 3 presents the system design architecture that includes an optical part and electrical part. In addition, the sensor's calibration and its response combined with a gas-liquid separator were determined in an indoor experiment, which is described in Section 4. Finally, the conclusions and future research prospects of carbon isotopic ratio measurement are illustrated in Section 5.
## 3 Findings
### Core Elements Underpinning SCM in the Construction Industry
#### 3.2.1 Strategic Management  
This subject area comprises three elements, namely, 'global strategy', 'change management' and 'capability development'. Supply chains are networks of independent but collaborating entities, where the performance of each of them depends on the performance of their partners [36]. Therefore, a global strategy pointing towards an emphasis on holistic systems is required to pursue competitive advantage. It should properly specify the participating entities and the relationships among them to achieve high value generation [37; 38], extending its scope to reach participants many layers up and down in the SC tiers [39]. This is not an easy task, especially considering the large number of participants working together in a project-based temporary manner [37]. However, as supply chains are becoming increasingly dependent on small- and medium-sized enterprises, which perform most project activities [20; 39], a global strategy is essential to create the conditions and enhance the possibilities of successful SCM implementation [23].  
A strategic change management approach is recommended to face the conservative nature of the industry. Organizations ought to show a willingness to change their culture, structure and processes [42]. To this end, strategies should focus on the dissatisfaction with the status quo, highlighting the need for transformational change and sensitizing organizations to the benefits that SCM offer both to themselves and their SC [40]. Communicating and convincing staff about what the change is and why it is happening, coupled with top management support [43] and the figure of a change champion [42], are essential tools to overcome resistance. In addition to the motivation, there is a need to promote a proper understanding of the concepts underlying SCM [23] and develop the competencies required to work with them [42]. This entails mobilizing and educate key SC stakeholders, providing training programs to employees at all organizational levels [40; 45] and active guidance in the practical context [42]. Such approaches, aimed at the development of knowledge, competencies and experience, are critical to achieve goals and contribute to both short-term and long-term success [20].  
\begin{table}
\begin{tabular}{c c c} \hline
**Subject Area** & **Element** & **References** \\ \hline Best Practices & Continuous improvement & [9; 10; 11; 14; 16; 17; 18; 20; 23; 37; 38; 62; 87; 88; 89] \\  & Lean thinking & [15; 66; 75; 90; 91] \\  & Just-in-time & [15; 54; 92; 93] \\  & Offsite manufacturing & [4, 15; 54; 57; 66; 89; 93; 94] \\  & Sustainability & [16; 40; 43; 45; 46; 49; 64; 70; 81; 95; 96; 97; 98; 99; 10; 116; 117; 118; 12; 13; 14; 15; 16; 17; 18; 19; 120; 121; 14; 16; 122; 123; 124; 125; 126; 127; 128; 129; 130; 131; 132; 133; 134; 135; 136; 137; 138;
# Fully Reconfigurable Photonic Filter for Flexible Payloads
## 1 Introduction
Satellite constellations, intended for varied applications such as global internet provision, Earth observation, and remote sensing, require payloads with the agility to reconfigure bandwidth allocation, optimize coverage areas, and flexibly reallocate frequency bands to ensure optimal performance in response to shifting user demands and emerging technological paradigms. Therefore, the development of adaptable and responsive satellite communication payloads becomes imperative to meet the ever-changing demands of a rapidly evolving communication landscape. At the heart of a multifaceted telecommunications payload lies a crucial element: the reconfigurable Radio Frequency (RF) front-end. This component embodies a band-pass filter (BPF) that stands as a cornerstone, offering adaptability through its adjustable central frequency and bandwidth. The BPF's flexibility to alter its central frequency and bandwidth empowers the system to accommodate diverse communication needs and spectral requirements. By fine-tuning these parameters, the RF front-end can efficiently isolate and transmit specific frequency bands while suppressing unwanted signals, ensuring enhanced signal purity and integrity. This adaptability proves pivotal in scenarios where varying signal conditions, frequency allocations, or interference patterns demand rapid and precise adjustments to optimize signal reception and transmission within telecommunications systems. Additionally, the reconfigurability of the RF front-end facilitates dynamic responses to changing communication standards, making it an indispensable asset for versatile and future-proof satellite- and space-based communication architectures. In 2013, the European Space Agency (ESA) established specific requirements, as outlined in Table 1, for photonic BPF. In conclusion, photonic transceivers rely on filters as essential components for various functionalities within their architecture. These filters play a crucial role in signal processing, allowing for the precise manipulation of optical signals. By incorporating these filters directly into photonic integrated circuits (PICs), transceivers experience significant advancements over conventional RF electronic technology. The integration of filters into the PICs enables miniaturization and consolidation of numerous functionalities onto a single chip. This integration not only reduces the SWaP footprint but also enhances the overall data management capabilities. The compactness and efficiency achieved through these integrated filters contribute to streamlined data transmission, increased signal purity, and improved system performance [7; 8].
# Study on Underwater Target Tracking Technology Based on an LSTM-Kalman Filtering Method
## 3 Simulation
### Construction of Array Signals and Generation of Datasets
#### 3.1.2 Generation of Dataset Samples for the Target Tracking Model  
The five targets in Section 3.1.1 moved along different trajectories. According to the Kalman filtering method, the intermediate calculated values at each time (namely, the expected prediction state \(\overset{\star}{\mathbf{X}}_{k+1,k}\) at the current time, the Kalman gain \(\mathbf{K}_{k+1}\), and the difference \(\alpha\) between the observed value and the expected prediction state) were used as the inputs for the LSTM network and the actual state of the moving target \(\mathbf{Z}_{k+1}\) was used as the label output. We simulated the Kalman estimation parameters of the above five types of targets in this experiment. Each type of target had 80 similar samples and the same characteristics. Each
## 3 Materials and Methods
### Model Construction
#### 3.2.2 DCSInformer Model  
The Informer model is an efficient deep learning model specifically designed to address long-series time-series prediction problems [17]. As an improved variant of the traditional Transformer model, the Informer excels in both natural language processing and time-series data processing [14]. The overall architecture of the Informer model, as shown in Figure 2, comprises two main components: an encoder and a decoder.  
However, it still faces several limitations. The lack of hierarchical feature processing across different time scales makes it difficult to balance long-term and short-term dependencies effectively [35]. This limitation is particularly evident in time series with high-frequency fluctuations or abrupt trends, where the model underperforms in predicting short-term features, thereby affecting trend accuracy. Further optimization is required to fully address the computational challenges posed by long-term time-series forecasting.  
Figure 1: STL-DCSInformer-ETS hybrid model.  
Therefore, this article proposes a DCSInformer model that combines dilated causal convolution and multi-scale feature extraction, significantly enhancing its ability to capture long time series and features across different time scales, making it particularly suitable for medium- and long-term forecasts, as shown in Figure 3.  
Therefore, this article proposes a DCSInformer model. The DCSInformer component introduces innovative techniques that differentiates it from previous hybrid models. Unlike traditional models that primarily focus on capturing short-term fluctuations, the DCSInformer component effectively handles both short-term variations and long-term dependencies simultaneously. This is achieved through the use of dilated causal convolution, which allows the model to capture broader temporal relationships, and a multi-scale feature extraction mechanism that enhances its ability to process varying time scales. These advancements significantly improve the model's ability to predict complex FMCG sales patterns, addressing the limitations of conventional forecasting methods, which often struggle to capture long-term trends. By incorporating these novel features, the DCSInformer model  
Figure 3: The DCSInformer model architecture diagram, illustrating the various components of the model, including the Max Pooling, Multi-Head Attention, and Self-Attention mechanisms, which work together to process and capture features from the input time series (inputs \(X_{t}\)).  
Figure 2: Informer model architecture diagram.  
provides a more accurate and robust solution for medium- and long-term sales forecasting in dynamic and volatile markets.
# Tracked Wall-Climbing Robot for Calibration of Large Vertical Metal Tanks
## 1 Introduction
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Type** & **Payload Capacity** & **Speed** & **Limitation** & **References** \\ \hline Arms and Legs & Medium & Slow & Complex mechanism & [15; 16] \\ Wheels & Medium & Fast & Small contact face & [4; 5; 6] \\ Tracks & High & Fast & Low steering capacity & [3; 7] \\ Wires and Rails & High & Fast & Wires or rails needed & [17] \\ Sliding Frame & High & Medium & Discontinuous locomotion & [18] \\ Sandwich & High & Fast & Two carts needed to climb & [19] \\  & & & the nonferromagnetic & \\  & & & surface & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of locomotion mechanisms. used. Magnetic wheel-type mechanisms are used to obtain flexible mobility [22]. However, their contact face is small, which means that a part of the magnetic unit is far from the metal surface, and the adhesion force is correspondingly small. Track-type mechanisms are preferred for their greater contact surface and stable adhesion force. However, it is difficult for them to change direction, and this could cause damage to the walls. A non-contact magnetic adhesion mechanism was developed to obtain simultaneous flexible mobility and greater adhesion forces, but its mechanism is complex and requires additional magnets, which increases the weight [5]. In this study, a robot with climbing ability is proposed to replace the manually-lifted trolley used in the traditional optical vertical line method. As the robot is expected to move in only one direction during the calibration of a single horizontal station, the steering capability is not a high priority. In addition, considering their lightness and reliability, track-type locomotion and permanent magnet adhesion mechanisms are suitable for the calibration of large vertical metal tanks. The scale is designed to be collapsible to avoid colliding with the fire pipelines. Considering that the surface of the tanks could be rusty, a shovel-like rust remover can be mounted to remove the rust and improve the contact between the adhesion mechanism and the surface. This study is organized as follows: Section 2 presents an overview of the tracked wall-climbing robot (TWCR) and introduces its primary characteristics, including the mechanical architecture, control system and the capacity calibration system, intended for the capacity calibration of large vertical metal tanks; the prototype, field tests and result are discussed in Section 3; and Section 4 presents the conclusions.
# Directional Paging for 5G Communications Based on Partitioned User ID
## 1 Introduction
In mmWave 5G communications, beam sweeping is performed to cover a spatial area by transmitting various beams in different time slots in a deliberate fashion [8; 10]. Thus, directional paging transmission over several beams would require multiple time slots to cover the entire cell, while legacy omnidirectional broadcast is possible in only one time slot per Paging Occasion (PO) [10]. This is a crucial challenge since the network's resource requirement for paging transmission would increase with the increase of the number of beams in directional 5G communications. We present a novel Partitioned UE ID-based Directional Paging (PIDP) mechanism to reduce paging resource overheads in 5G communications. The main contributions of this paper are summarized as follows:  
1. The same paging message that is transmitted over several beams is majorly comprised of UE IDs of all the UE, configured in the PO. In PIDP, we propose to reduce the number of UE ID bits included in the paging message to reduce the overall paging resource overheads of the network. 2. We present a novel method for the configuration of POs in the IDRX cycle and an effective distribution of UE over various POs. The proposed configuration of POs in PIDP ensures that  
Figure 1: Directional air interface in 5G communications. the network is able to page all the UE uniquely despite the fact that only partial UE IDs are transmitted in the paging message. 3. We perform numerical analysis to ascertain the advantages of PIDP. Paging resource overheads, the number of bits and time slots required for paging transmission and, subsequently, the base station's power saving gain are analyzed with respect to variations in the number of beams and paging rate. Recent 5G parameters are considered for the analysis. We compare our proposed PIDP mechanism to directional paging transmission with full UE ID. The proposed PIDP mechanism manifests more than a 15% power saving gain compared to directional paging transmission while using the full UE ID. By reducing the number of bits included in the paging message transmitted by the network and by efficient reconfiguration of the POs, the PIDP mechanism reduces the network's paging overheads. It is to be noted that each UE wakes up for a specific PO and monitors only one PO per IDRX cycle. Thus, the PIDP mechanism does not have any negative impact on the UE energy consumption, while at the same time, it achieves power saving in the network. The rest of this paper is organized as follows. In Section 2, we present the related work with a focus on the fundamentals of paging from both the UE's and network's perspective. The PIDP mechanism for directional paging and its system model are presented in Section 3. In Section 4, we evaluate the system performance of our PIDP proposal by varying the values of different parameters like UE ID size, number of beams and number of UE per PO. Finally, we draw conclusions in Section 5.