esa-sceva/satcom-chunk-collection huggingface dataset:
# Global Power Dynamics in the Contemporary Space System
## 5 System Dynamics--Global Power Dynamics
### Reconfiguration of Strategic Alliances
The ongoing rise of China and the unique relationship between Russia and China have compelled India to seek new strategic partnerships. In this context, India established a strategic partnership with the United States, as evidenced by India's accession to the Artemis Accords in June 2023. Nevertheless, India has endeavored to preserve an independent and occasionally pendular position, maintaining relations with Russia, China, or Brazil within the context of the BRICS. Japan and the European Union countries have maintained a robust alliance with the United States since the 20 th century, initially as key partners in the International Space Station (ISS), and most recently by joining the Artemis Accords. However, there are slight differences between them. Japan has demonstrated a consistent alignment with U.S. interests, while the EU has expressed a commitment to reinforcing Europe's autonomy in accessing and utilizing space and strengthening Europe's role as a leading actor in the space sector [59; 60; 61; 62]. The growing influence of emerging middle powers and other developing countries has prompted the adoption of various strategies and mechanisms for space cooperation. For instance, African and Latin American countries have entered into bilateral agreements with space powers to gain access to space technology, as is the case with China. Additionally, a majority of developing countries have participated in the establishment of regional space processes designed to create synergies, including the Latin American and Caribbean Space Agency (ALCE) initiative in Latin America, the Asia-Pacific Space Cooperation Organization (APSCO) in Southeast Asia, and the recently established African Space Agency in Africa.
## 4 Implementation and Experimental Result  
The basic system we implemented is a vertically diffused horizontal parallax only (VD-HPO) hologram system. This system is the one without pupil tracking module, having a 20\({}^{\circ}\) vertical viewing angle and a nonsupported vertical parallax, which is similar to rainbow hologram. With pupil tracking module added, the system becomes a full parallax hologram system.  
Schematic diagram of the basic display system and photo of the final system shape are shown in Figure 7. The system consists of an SLM module consisting of laser light source and three DMDs, _4-f_ relay optics for image magnification and noise filtering, temporal multiplexing module consisting of a scanning motor and light path redirecting optics, and volumetric image floating optics consisting of double parabolic mirrors or equivalent optics.  
Three-hundred-and-sisty-degree viewable volumetric color hologram image is rendered in the display by coalition of 1024 view-dependent holograms, which are generated from 3D data consisting  
Figure 6: Vertical parallax support by using pupil tracking and dynamic hologram update. (**a**) Conceptual model and (**b**) 180\({}^{\circ}\) pupil tracking module and hologram images seen at four different vertical viewing position.  
Figure 7: (**a**) Schematic configuration. (**b**) Photo of the implemented system. (**c**) Demonstration.  
of color and depth image for each viewpoint from a CG model or scanning real objects. Figure 8 shows the whole signal chain from 360\({}^{\circ}\) 3D data capture from real moving or static objects, possibly including humans, CGH computation, and real-time hologram data transmission via data server with high speed interface up to display system. There are several highly complicated signal processing tasks involved in the chain. The 360\({}^{\circ}\) data capture part has two alternative setups: one consists of a single depth sensing camera with a turntable for a static object and the other, for moving objects, consists of multiple synchronized depth sensing cameras arranged 120 degrees apart. Image registration and stitching, smoothing, and hole-filling algorithms are applied to get point cloud data which is optionally converted to 3D mesh model. Finally RGB and depth image data are extracted via virtual camera setting for required number of viewpoints of the tabletop display. This data is input to the CGH computation module, algorithm of which is calculating wave propagation model of the display optics, outputting hologram fringe patterns. Details of this CGH are presented in reference [23]. The hologram data is uploaded to data server and finally transmitted to the SLM module of the display in real time via high speed interface from data server to the display system.  
Images of optically reconstructed hologram images from the implemented display system are shown in Figure 9.  
Figure 8: Signal processing chain of the tabletop holographic display system.  
Figure 9: Optically-reconstructed hologram image captured from different horizontal viewpoints: (**a**) CG model and (**b**) real human object.
# Efficient Bloom Filter-Based Routing Protocol for Scalable Mobile Networks +
## 2 Methodology
### BF with Temporal Information  
By enhancing the capabilities of the standard BF structure, the HRAN routing protocol will be able to determine whether an item has been recently added. A novel BF variant, the TAB (Time Aware Bloom), was developed for this purpose.  
As can be seen in Figure 2, this is a pair of BFs (sticky and plain filter (P&S)) with the same size M and the same set of HFs K. To check whether the final slot has been occupied by a bit, the sticky filter is applied(\(\delta\)n). The plain filter contains the bits entered during the previous and previous time periods (\(\delta\)n \(|\)\(|\)\(\delta\)n\(-\)1). Vectors always employ TAB filters; here, we detail how to perform insert, search, and update actions while using TAB filters.  
The procedure uses the standard insertion operation on the P&S filters already present in the vector to add an element X at position I of the TAB filters. To determine the k locations, we must first execute the K HFs on the inserted element. In these roles, both BFs will have the value of 1.  
When looking for the value Y in a TAB array, only the array's simple filters are examined. The search element is hashed with the KHFs, and then the bits of each acquired location are checked in a series of simple filters to see whether they all contain the value 1. In such case, the search was successful. If this match is never discovered after going through all of the filters in the array, then the item is not found.  
Figure 1: HRAN process: representation of the temperature gradient at node D; thermal tunneling of the current path between node A and node D, random search and directed search between node B and node D.  
Figure 2: TAB filter.
## 3 Performance Evaluation
### Experimental Setup  
The experimental setup was based on a hardware platform employing a _Texas Instruments_ (TI) EZ430-RF2500 receiver [17]. This device includes both an MSP430F2274 microcontroller along with a CC2500 2.4 GHz transceiver. The CC2500 was configured to run at 250 kbps. A single device, labeled as AP, was connected via a USB to a Serial link, running at 115,200 BAUD, to the host computer. All other devices, labeled as EDs, were battery powered. In this implementation, the AP acts as a bridge between the host and the end devices. All routing and power control calculations were done on the host. The host was a laptop computer with an Intel(r) Core(tm) i5-2410M CPU and 4 GB of RAM.
## 3 UAV Deployment and Trajectory
### A Static UAV
#### 3.1.2 UAV RS  
Compared with UAV BS, there have been less studies on UAV RS due to the limited capacity to provide a wireless connectivity to a small amount of user groups. However, it is also important on mission-critical communications such as military and first respond communications. Ref. [42] considers the placement of multiple UAVs as a facility location problem (FLP) to improve the connectivity by grouping mobile UEs to the corresponding UAV with the shortest distance, thereby maximizing the capacity of the downlink. Ref. [43] proposes a UAV deployment algorithm for multiple UAV relay systems to maximize an average data rate of UEs and to provide a reliable relay connectivity to an isolate UE simultaneously. The minimum number of UAVs and the minimum number of transmission time slots to improve an average data rate are derived, and the performance comparison between multiple UAVs and single UAV relay systems with respect to the position of an isolated UE is presented. Ref. [44] applies a non-orthogonal transmission at BS in a single UAV relay system to increase the signal to interference plus noise ratio (SINR) at UEs within BS coverage. A joint resource allocation algorithm is proposed to optimize the transmit period and power of signals at both BS and UAV. The proposed algorithm maximizes the overall throughput of UEs while guaranteeing a seamless UAV relay transmission by analyzing the outage probability and utilizing the power control strategy. This study is extended in [45], where the minimum required transmission time slots between terrestrial BS and UAV and between UAV and an isolated user are first determined, and the feasible area within that UAV can be deployed is reduced. The proposed UAV deployment and time allocation algorithm achieves a fast and optimal UAV deployment due to the small feasible search area. Ref. [46] investigates how the type of wireless backhaul links (i.e., network-centric or user-centric) would affect the number of covered users. The optimal three-dimensional (3D) backhaul-aware placement of a single UAV RS is obtained to maximize both the total number of served users and sum-rates. The robustness of the network is also examined with respect to the user distributions. A sum-rate maximization for UAV-enabled two-way relay systems is considered in [47], where the local optimal solution for joint optimization of time slot pairing, transmit power allocation, and the UAV deployment is obtained.
## 4 Discussion
Regarding the results of GBI, all three devices contributed to the improvement of QOL in all cases. There have been studies on ADHEAR and QOL [20; 21], though the method of QOL evaluation was different, and they also reported that the use of ADHEAR improved QOL. There are examples in the literature where GBI is used for QOL evaluation, and that also acknowledge the improvement of QOL due to using Baha. In our study, the GBI score for Baha was relatively lower to those reported in previous studies (38 on average), but it is considered that factors such as race, hearing level, and the duration of using the device were different [22; 23]. Additionally, there have been works in the literature comparingADHEAR with Baha on the point of QOL, but there have been no reports comparing the degree of QOL improvement using these three devices. In the work comparing ADHEAR with Baha, there was no significant difference between two devices [17], and in this study, no clear relationship was found among the three devices in the QOL evaluation, and the results of the devices differed for each case. The degree of improvement in QOL is often inconsistent with the results of objective tests such as sound field audiometry and sound localization tests. Various subjective factors, such as comfortability and sound quality, are thought to be related to improvements in QOL. Baha may be suitable for patients who desire a large gain in hearing. However, this is not an option for patients who refuse surgery. CC-HA and ADHEAR are options suitable for patients who refuse surgery. ADHEAR claims simplicity as its merit, however, its gain tends to be smaller than that of CC-HA. In addition, comfort in wearing the device and its aesthetics are said to be advantages of using ADHEAR. In this study, however, the device easily peeled off from the skin of a few patients, and it was unexpectedly noticeable compared to others. We concluded that the decision of which device is most satisfactory depends on the patient's impression, regardless of the gain and score of the sound source localization test. Therefore, we believe that it is necessary to provide an opportunity for comparative trials and consultations with each patient in the process of device selection.
## 2 Methodology
### Objective Function
#### 2.3.1 Data Fitting Residual Term and Model Term  
The data fitting residual term is defined by the following equation (Equation (6)):  
\[\varphi_{d}=\left\{W_{d}[D-F(M)]\right\}^{T}\left\{W_{d}[D-F(M)]\right\} \tag{6}\]  
Figure 1: Schematic diagram of the rectangular grid of electrode cells.  
The model term is defined by the following Equation (7):  
\[\varphi_{s}=\Big{(}M-M_{ref}\Big{)}^{T}C_{m}^{-1}\Big{(}M-M_{ref}\Big{)} \tag{7}\]  
In Equations (6) and (7), \(\mathrm{D}=[\mathrm{d}_{1}\ \mathrm{d}_{2}\cdots\mathrm{d}_{\mathrm{nt}}]^{T}\) is the observed electric field response dataset, with dimensions of \((\mathrm{nd}\times\mathrm{nt})\times 1\). not refers to the number of data at one time step, nt refers to the number of time steps, and \(\mathrm{d}_{\mathrm{nt}}\) is the electric field response data measured at nt time step. \(\mathrm{F}(\mathrm{M})\) is forward response; \(\mathrm{W}_{\mathrm{d}}\) is the data weighting matrix: \(\mathrm{W}_{\mathrm{d}}=\mathrm{diag}\Big{\{}1/\sigma_{1},1/\sigma_{2}, \cdots,1/\sigma_{(\mathrm{nt}\times\mathrm{nd})}\Big{\}}\). \(\mathrm{M}=[\mathrm{m}_{1}\ \mathrm{m}_{2}\ \cdots\mathrm{m}_{\mathrm{nt}}]^{T}\) is the resistivity parameter of the model corresponding to each observation time step. \(\mathrm{M}_{\mathrm{ref}}\) is the reference model resistivity parameter. \(\mathrm{M}\) is an iterative, and the dimensions of \(\mathrm{M}_{\mathrm{ref}}\) and \(\mathrm{M}\) are \((\mathrm{nm}\times\mathrm{nt})\times 1\). nm represents the number of rectangular grids, and \(\mathrm{C}_{\mathrm{m}}^{-1}\) is the linear smooth matrix of the model.
## 4 Network Model
### Inter-Cell-Interference  
Inter-Cell Interference (ICI) is one of the main challenges that degrades the performance of wireless network, particularly at the cell-edge areas. ICI is caused by reusing the same time-frequency resources is used in nearby cells, which cause co-channel interference. ICI increase significantly in the next generation UDN network architecture, because network deployment is with high densification. With the dramatic increase of ICI from surrounding cells, ICI coordination (ICIC) is necessary to cope  
Figure 5: Definition of \(d_{\mathrm{2D}}\) and \(d_{\mathrm{3D}}\) in path loss model.  
with excess interference. One of the most effective ICIC is the Fractional Frequency Reuse (FFR) [36]. The impact of ICI from the surrounding cells has been considered in this work in order to realise an accurate traffic and fairness measurements of the network model.
# Expansion Lemma--Variations and Applications to Polynomial-Time Preprocessing
## 4 (Basic) Expansion Lemma and Applications
### Undirected Feedback Vertex Set  
Thomasse [6] gave a kernel with \(\mathcal{O}(k^{2})\) vertices for the Feedback Vertex Set. The main idea is to use the expansion lemma to bind the maximum degree of the graph to \(\mathcal{O}(k)\).  
Suppose a vertex \(v\) in the graph has a degree more than \(11k\). In polynomial time, one can determine [11; 35], where \(v\) is part of at least \(k+1\) cycles with \(v\) as the only intersection (in which case \(v\) is forced into the solution) or find a set \(S_{v}\) with a size of, at most, \(3k\), which hits all of the cycles passing through \(v\). The next step is to bind the number of components in the graph \(G-(\{v\}\cup S_{v})\) that has a vertex adjacent to \(v\). Firstly, note that \(v\) is adjacent to exactly one vertex in such components; otherwise, there is a cycle containing \(v\) not intersecting \(S_{v}\). Such components can be disregarded in this graph, which can contain cycles. Note that the number of such components is at most \(k\) as they form disjointed cycles.  
Consider the auxiliary bipartite graph where one partition is \(S_{v}\) and the other partition is the remaining components of \(G-(\{v\}\cup S_{v})\) and add edges if a vertex in \(S_{v}\) is incident to a vertex in the component. If the number of components is more than \(6k\), then Lemma 3 can be used with \(q=2\) to find subsets with each vertex in \(S_{v}\) forming a 2-expansion. Note that since \(v\) is adjacent to these components, this provides us with a set of cycles containing \(v\) corresponding to each 2-expansion. Using the presence of these cycles, it can be proven that any cycles hit by vertices in these components can be hit by either \(v\) or all the vertices in the expansion subset of \(S_{v}\). The authors encode this by creating disjoint 2-cycles containing \(v\) and each vertex in the expansion subset of \(S_{v}\) if not already present.  
This bounds the number of such connected components to \(2|S_{v}|\leq 6k\). Thus, the number of vertices adjacent to \(v\) is bounded by \(6k+|S_{v}|+k\leq 10k\). At most, \(k\) of these vertices can have 2 edges to \(v\) as we can delete \(v\) if it is a part of the \(k+1\) cycle intersecting only at \(v\). Thus, the degree of \(v\) is bounded by \(11k\).  
From other reduction rules, it is possible to prove that the graph has a minimum degree of three. Finally, in this case, it can be proved that Feedback Vertex Set (defined in Box 13) admits a kernel with \(\mathcal{O}(k\Delta)\) vertices where \(\Delta\) is the maximum degree of \(G\). Thus, we have a kernel with \(\mathcal{O}(k^{2})\) vertices as \(\Delta\leq 11k\).  
Recently, Iwata [36] obtained a kernel with \(\mathcal{O}(k^{2})\) vertices in \(\mathcal{O}(m+n)\) time.  
**Box 13**.: Undirected Feedback Vertex Set Problem.  
Feedback Vertex Set (FVS)  
**Input:**_An undirected graph \(G=(V,E)\) and an integer \(k\)._  
**Parameter:**_k._  
**Question:**_Is there a set \(S\subseteq V(G)\) of, at most, \(k\) vertices, such that \(G-S\) is acyclic?_
# Performance Evaluation of Random Access Methods for IoT-over-Satellite
## 4 Emulation Results and Discussion
### Access Methods Comparison  
To compare the impact of the different access methods introduced in Section 3.3, we depict in Figure 12 the BER for the different access models against varying transmit power. This figure shows that case III has the best performance, i.e., the BER is lowest in case III, while case I had the worst BER. The finding fulfills our expectation since the BER in case I is impaired with both the lack of satellite availability and packet collision. In contrast, the four repeated transmissions reduce the BER at least by 10% when compared to case I at \(P_{t}=10\) dBm. To further investigate the performance of case III, we emulate the trend of PER versus different packet repetition number \(L\) as shown in Figure 13. The figure shows that PER is improved with the increasing number of repetitions. It also indicates that the rate of improvement slows down with higher repetition.  
Figure 11: The packet successful rate for case I as a function of the number of satellites in the constellation. The IoT devices transmit power is \(P_{t}=27\) dBm.  
Figure 10: The average BER for case I while varying the target IoT device latitude, showing the Walker-Star for \(i=87^{\circ}\) and Walker-Delta for \(i=54^{\circ}\), the rest of the simulation parameters are listed in Table 1.
# Crack Location and Degree Detection Method Based on YOLOX Model
## 4 Experimental Results and Analysis
### Model Evaluation Metrics
#### 4.1.1 AP and mAP  
First, IOU is a measurable standard in the accuracy of detecting corresponding targets in a specific dataset. According to the experimental results, if the crack type is positive (P) and the model detection is P, it is marked as a true positive. In general, when IOU \(\geq\) 0.5, the detection result can be considered as a true positive (TP). Similarly, if the crack type is negative (N) but the model detection is P, it is marked as a false positive (FP), that is, when IOU \(<\) 0.5, the detection result is considered as a false positive. If the crack type is P, but the model does not detect the crack type, it is marked as a false negative (FN), and the model does not detect the ground truth box at this time. If the crack type is N but the model detection is correct, it is marked as a true negative (TN).  
Then, Precision and Recall are defined, which are two basic evaluating indicators in object detection, as shown in Equations (5) and (6). Precision represents the proportion of correctly recognized targets among all detected targets, while Recall represents the proportion of correctly recognized targets among all detected positive samples:  
\[\text{Precision}= \frac{\text{TP}}{\text{TP}+\text{FP}} \tag{6}\] \[\text{Recall}= \frac{\text{TP}}{\text{TP}+\text{FN}} \tag{7}\]  
where TP is the number of the crack type correctly recognized, TN is the number of the non-crack type correctly recognized, FP is the number of the non-crack type regarded as the crack type, and FN is the number of the crack type regarded as the non-crack type.  
Finally, AP, as shown in Equation (8), could be calculated by the area under the Precision and Recall curve, which completely considers the impact of the Precision and Recall. In addition, the average value of each crack type of AP in the data set is mAP, which can reflect the accuracy of crack detection. Additionally, mAP can be used as a criterion for the comparison among difference detection models.  
\[\text{AP}=\int_{0}^{1}\text{P(R)}\text{ dR} \tag{8}\]
## 3 Energy Management Strategies for HEVs
### Rule-Based Control Strategies
#### 3.1.1 Deterministic Rule-Based Control Strategy
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rule Category** & **Design Basis** & **Implementation Method** & **Representative Studies** \\ \hline Load-Zone & Division of engine efficiency map & Matching demanded power & [PERSON] et al. (2004) [95] \\ Partitioning Rules & into high/medium/low- & efficiency zones & [PERSON] et al. (2004) [95] \\ Mode Shift Rules & Co-optimization of gear ratio & Decoupling engine & \\  & and SOC & speed through & [PERSON] et al. (2020) [82] \\ Dynamic & Rate of change in accelerator & Predictive adjustment of & \\ Compensation Rules & pedal position & motor compensation power & [PERSON] et al. (2017) [96] \\ Expert Knowledge- & Encoding engineering experience & Embedding rule constraints & \\ Integrated Rules & into DRL reward functions & in DDPG algorithms & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Key Implementation Rules for Rule-Based EMSs. (2022) [103] proposed an adaptive hierarchical approach that integrates DDPG and heuristic domain knowledge for HEVs. [PERSON] et al. (2022) [104] developed a rule-based control strategy for power distribution in a parallel HEV based on driver demand. These studies demonstrate the effectiveness of deterministic rule-based control strategies in optimizing energy management for HEVs, showcasing improvements in energy consumption costs, comfort maintenance, and fuel efficiency. The rule-based deterministic approach, which relies on engineering expertise, operational mode segmentation, and static energy efficiency maps, offers straightforward comprehension and implementation. However, its inability to adapt to fluctuating operating conditions and dynamic demands poses challenges in achieving optimal control. In order to optimize the performance and adapt to the working conditions in real time, fuzzy control began to be integrated into the rule-based control.
# A Study on the Vehicle Routing Planning Method for Fresh Food Distribution
## 2 Problem Description and Model Construction  
By studying location and path problems, a bi-level programming model is constructed to solve the location and path planning problems of a multi-center single vehicle type with time window constraints and an objective function of minimizing the total cost.