esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data and Methodology
### Explanatory Variables
#### 3.2.1 Socioeconomic and Transportation Accessibility Variables  
The initial set of socioeconomic and transportation accessibility variables includes gross resident product, population, housing, road mileage, number of bus stops, and distance to the city center. Gross resident product data are derived from actual production values adjusted by nighttime light data [91]. Population data come from the open-source WorldPop database ([[https://www.worldpop.org/](https://www.worldpop.org/)]([https://www.worldpop.org/](https://www.worldpop.org/)) (accessed on 20 February 2025)) and have been calibrated based on China's Seventh National Population Census. Housing information, such as prices, floor area, construction year, addresses, and names, is obtained from Beike ([[https://sh.ke.com/](https://sh.ke.com/)]([https://sh.ke.com/](https://sh.ke.com/)) (accessed on 20 February 2025)), one of China's largest and most reputable online real estate agencies. From 9280 records of second-hand housing transactions, 6260 records within the study area were selected, and these were used to calculate the average housing price in each neighborhood. Road mileage data are collected from OpenStreetMap (OSM) ([[https://www.openstreetmap.org/](https://www.openstreetmap.org/)]([https://www.openstreetmap.org/](https://www.openstreetmap.org/)) (accessed on 20 February  
Figure 1: Location of the study area, Haikou, in China.  
2025))), including major roads, primary roads, secondary roads, and urban side streets. Since Haikou's subway system is still in the planning stage, buses currently serve as the city's primary form of public transit, leading this study to select the number of bus stops as an indicator of local bus accessibility. All of these variables are aggregated at the grid-cell level. Distance to the city center is measured as the geodesic distance from each grid cell's centroid to the Hainan Center (110.35522\({}^{\circ}\) E, 20.01773\({}^{\circ}\) N) [92].
# TRC-Based High-Precision Spot Position Detection in Inter-Satellite Laser Communication
## 2 The Performance of the No Independent Beacon Light ATP System  
As Figure 1 shows, the no independent beacon light Is-OWC terminal does not have a separate beacon laser with a large divergence angle, but uses a narrower signal light beam to complete space capture and tracking. Therefore, the energy of the light irradiating to the detector in the ATP system is extremely small. For example, when the light beam propagation distance is 50,000 km, the optical antenna aperture is 200 mm, and the beam divergence angle is 30 \(\upmu\)rad, the geometric attenuation of the incident beam will reach 77 dB. In this case, the received SNR of the ATP system will be lower than \(-17\) dB.  
As a coarse tracking detector in the system, QD can be seen as the splicing of four independent avalanche photodiodes (APDs) in a rectangular coordinate system, with each APD corresponding to a coordinate quadrant. When incident light is irradiated on the photosensitive surface of QD, each APD will induce a corresponding photocurrent, and the amplitude of each photocurrent is proportional to the energy of the received light beam in each quadrant.  
Since the proportional relationship of the output photocurrents can correspond to the coordinate position of the light spot, we can define a normalized variable that can quantitatively reflect the offset value of the light spot position relative to the coordinate origin, which is expressed as [17; 18; 19]:  
\[\Delta x\ =\ \frac{(I_{A}+I_{D})-(I_{B}+I_{C})}{I_{A}+I_{B}+I_{C}+I_{D}} \tag{1}\]  
\[\Delta y\ =\ \frac{(I_{A}+I_{B})-(I_{C}+I_{D})}{I_{A}+I_{B}+I_{C}+I_{D}} \tag{2}\]  
where \(\Delta x\) and \(\Delta y\) are called the normalized offset value of the x-axis and y-axis, which quantitatively gives the offset position of the spot on the QD target surface relative to the center of the target surface.  
It can be seen from Equations (1) and (2) that using QD to detect the spot position essentially calculates the distribution ratio of the light beam energy irradiated to the four APDs. Furthermore, literature [20] proposed that the spot position detection accuracy is affected by the factors such as spot position, spot radius and SNR. Therefore, it is necessary to find a way to effectively improve the output SNR of QD according to the characteristics of the noise distribution.  
In QD-based ATP systems, the main noise is background radiation noise and thermal noise. The both noises can be equivalent to Gaussian noise whose power spectrum density can be regarded as evenly distributed over the entire frequency band [21; 22; 23]. If a cosine signal is used to modulate the intensity of the transmitting light, the spectrum of the corresponding photocurrent signal output by the QD will be a line spectrum signal. Therefore, the energy of the photocurrent signal will be concentrated on a certain frequency point, thereby improving the SNR at the local frequency point, so as to improve the detection accuracy of the photocurrent amplitude.
## 3 Results and Discussion
### Evaluation of Conformity of Shape and Dimensions
#### 3.2.1 Evaluation by Visual Observation  
For reliable visual observations, each subject wore tights, and a standing traction device was used to induce a standing posture identical to the state of static balance while wearing a wearable robot. After completing the preparation process, the 3 DP-pHRI, which was produced by estimating the location where the trunk and shank are equipped on the wearable robot, was directly matched to the human body shape of the subject, and the conformity of the shape and dimension was evaluated by visual observation (Figure 11).  
In the visual observation of the trunk 3 DP-pHRI, the trunk 3 DP-pHRI, which showed the highest conformity in shape and dimensions, was selected as the sizing system based on the degree of conformity for the curve shape of the rear vertical section and the degree of contact in the circumferential direction. In the visual observation of the shank 3 DP-pHRI, the shank 3 DP-pHRI, which showed the highest conformity in shape and dimensions, was selected as the sizing system based on the degree of conformity for the curve shape of the side vertical section and the degree of contact in the circumferential direction (Figure 11).  
Table 11 shows the results of selecting 18 trunk 3 DP-pHRI size systems and nine shank 3 DP-pHRI size systems according to the visual observation criteria for 10 persons with paraplegia. Trunk sections 4, 5, 9, and 16 were selected twice, and sections 6 and 10 were selected once. Shank sections 5, 10, and 14 were selected once, section 2 was twice, and section 7 was selected five times. The trunk and shank sizing systems selected showed conformity in shape and dimensions at an appropriate level for each subject. However, the shank system showed a biased tendency in the frequency of selection. Additionally, as with the selection results of trunk10-shank5 of Sub1, the trunk and shank sizing systems for each subject tended to be selected in different sections. This seems to have been caused by the body-shape specificity of people with paraplegia, who experience a decrease in the lower limb muscles because of disuse, which suggests that the shank 3 DP-pHRI size system requires adjustments.
## 3 Analysis of the Influencing Factors in Topology Optimization
### Material Properties  
It is well known that changes in material properties remarkably affect the results of static analysis, but further investigation is still required regarding the impact of material properties on the structure configuration in the TO.  
Table 3 presents the TO structure configuration corresponding to various material properties [43] under the conditions of a hexahedral mesh size of 2 mm and a tetrahedral mesh size of 4 mm. Figure 5 demonstrates the influence of various material properties on the compliance and computation time for both hexahedral and tetrahedral meshes. The results provided in Table 3 and Figure 5 reveal that regardless of whether a hexahedral or tetrahedral mesh is used, when the material properties undergo significant changes, the TO structure configuration remains almost unchanged and has only a slight impact on the compliance value. This phenomenon can also be explained by Equation (7). In TO calculations, the global stiffness matrix is closely related to the elastic modulus (\(E\)) and Poisson's ratio (\(\mu\)). The changes in these two factors lead to changes in the values of \(\mathbf{K}(\widetilde{\mathbf{x}})\), resulting in fluctuations in the compliance value. Furthermore, as demonstrated in Equation (1), the TO utilizes unit density as a design variable in the range of [0.001, 1]. The given elastic modulus (\(E\)) exhibits a transc
## 2 Background and Related Concepts
### Privacy and Security Requirements  
Privacy and security requirements define the security standards that policies and technologies in the BYOD context should meet. Standard terms include confidentiality, integrity, availability, non-repudiation, authentication, privacy preservation, and attack detection. The rationale for selecting these requirements and the underlying concepts will be discussed in Section 4.
# Introduction to Radar Scattering Application in Remote Sensing and Diagnostics: Review
## 4 Radar and Remote Sensing Applications
### Monitoring Terrestrial and Planetary Information  
A spaceborne radar can provide information about both terrestrial and planetary surfaces [95]. Radar astronomy allowed significantly improving our basic knowledge of planetary systems: for instance, a spacecraft radar system was applied in planetary missions to map the Venus surface (in a relatively low resolution) in the 1960s, and the unexpected 59.6-day rotation period of Mercury [96].  
A major contribution to the investigation of the surface of Venus came from the upgrade of the Arecibo's radar system, which allowed analyzing the planet surface at a high resolution. In the literature, some recent astrometry-based activities are radial velocity measurement and the discovery of measuring the position of a solar system using image-based echo [97; 98; 99; 100]. [PERSON] et al. employed the Sardinian radio telescope as a radar for the study of near-Earth objects and Space Debris [101].
# A Survey on Efforts to Evolve the Control Plane of Inter-Domain Routing
## 3 Background
### Traffic Engineering with BGP  
Control of the outbound network traffic is an easy task. Since an AS controls the decision process on its BGP routes, it can select each best path to reach a particular destination through its peers. The AS can rely on the _Local preference_ attribute, for example. By assigning appropriate values, the AS indicates which route should be considered as the best route to the BGP routers [29].  
However, it is not a simple task to control inbound network traffic because it requires techniques to be applied that do not guarantee its effectiveness [30]. A network operator can use the following techniques to influence the incoming network traffic with the BGP [31]:  
* Selective advertisement: to rely on selective advertisements and announce different route advertisements on different links;
* AS-path prepending: by default, the BGP prefers the route with the shortest AS-Path length when two or more routes exist to reach a particular prefix. Increasing the AS Path length of a particular route may change the way that the BGP selects the best routes and consequently how the other AS traffic is handled;
* Multi-Exit Discriminator (MED): MED is a hint to external neighbor routers about the preferred path into an AS that has multiple entry points. This suggestion can, or not, be acceptable for the neighbors;
* Communities: are \"tags\" associated with advertisement prefixes and appended pre-arranged communities can be used to influence path selection of other ASs if the neighbors consider those communities in their routing decision process.  
Therefore, network operators can only rely on the manipulation of the BGP's attributes to apply any traffic engineering technique. However, for inbound traffic engineering, those approaches only try to influence the routing decisions of external ASs, in order to obtain their desired inbound traffic distribution.  
Nonetheless, because each AS selects preferred routes based on its own policies, usually the BGP inbound techniques lead to a trial-and-error process with no guarantees of success. Therefore, the agreements, contracts and AS relationships dictate how the configuration of the BGP should be performed for a given AS.
## 5 Joint Optimization of FeICIC Parameters and Base-Station Density  
Due to the fact that MBSs are usually deployed by network operators, MBS density will change slowly and can be assumed to be constant for analysis simplification. Further simplification of the problem analysis, MBS transmit power in USF and PBS transmit power can also be assumed to be constant without considering power control. Moreover, the PSF ratio can be calculated according to Equation (7). Hence, the network EE is mainly impacted by pico CRE bias, power reduction factor, and PBS density under different UE density, i.e., network load. As MBS density is a constant value, we can first obtain the optimal value of the ratio between PBS density and MBS density \(\lambda_{p,m}\), denoted as \(\lambda_{p,m}^{*}\), to maximize the network EE. Then the optimal PBS density \(\lambda_{p}^{*}\) can be calculated by \(\lambda_{p}^{*}=\lambda_{m}\lambda_{p,m}^{*}\). Thus, the joint optimization problem with the object of network EE maximization can be formulated as follows:  
\[\begin{split}\underset{\rho,B_{p}\lambda_{p,m}}{\arg\max EE}\\ \text{s.t. }0&<B_{p}\leq 25\text{ dB}\\ 0&\leq\rho<1\\ 0&<\lambda_{p,m}\leq 30\end{split} \tag{15}\]  
However, the network EE is nonlinear with \(\lambda_{p,m}\), \(B_{p}\) and \(\rho\), which is difficult to obtain the optimal \(\lambda_{p,m}\), \(B_{p}\) and \(\rho\) at the same time with reasonable complexity. Note that the value ranges of \(\lambda_{p,m}\), \(B_{p}\) and \(\rho\) are limited, which make it possible to seek out the optimal values of \(\lambda_{p,m}\), \(B_{p}\) and \(\rho\) through a linear search algorithm by fixing two of these three variables, respectively. Therefore, we propose a heuristic algorithm to obtain the sub-optimal solution of the joint optimization problem in Equation (15). The proposed heuristic algorithm decomposes the original optimization problem into two sub-problems including FeICIC parameters optimization and PBS density optimization. For FeICIC parameters optimization, we first derive the approximate relation between \(B_{p}\) and \(\rho\). Then, we get the sub-optimal values of \(B_{p}\) and \(\rho\) with given \(\lambda_{p,m}\) by an alternating algorithm. For PBS density optimization, the optimal value of \(\lambda_{p,m}\) can be obtained by a linear search method based on fixed \(B_{p}\) and \(\rho\). Finally, we alternately solve two sub-problems to achieve globally optimal values of these variables.
## 4 Experiments
### Evaluation Metrics
#### 4.2.2 Root Mean Squared Error (RMSE):  
Root mean squared error (RMSE) can be calculated as follows:  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{N}(p_{i}-r_{i})^{2}} \tag{15}\]  
where \(n\) is the total number of ratings over all users, and \(p_{i}\) is the predicted rating for item \(i\), whereas \(r_{i}\) represents the actual rating of item \(i\). RMSE amplifies the contributions of the absolute errors between the predictions and the true values.  
Although the accuracy metrics have greatly helped in the field of the recommender systems, accurate recommendations are sometimes not the ones that are most useful to users. For example, users may prefer to be recommended with items that are unfamiliar to them, rather than the old favorites that they may not want.
# A Forwarding Latency Optimization Method for Software Data Plane Based on Spin-Polling
## 2 Related Work
### Spin-Polling Mechanism  
The traditional network stack processes packets based on an _interrupt_ mechanism. The NIC will generate a hardware-level interrupt signal whenever a packet arrives to notify the CPU. After the CPU receives an interrupt signal, it will copy the packets from the direct memory access (DMA) memory area to the kernel _mbuff_ structures at an appropriate time. This mechanism seems to be acceptable in scenarios with low network traffic load [12]. However, when the network traffic load becomes heavy, the interrupt mechanism will generate too many interrupt signals, and the generation, transmission, and processing of these signals will cause much overhead [19]. These excessive overheads make traditional network stacks unable to cope with ever-increasing traffic rates.  
Figure 1: The basic architecture of software data plane.  
In order to cope with the pressure of high network traffic load, modern high performance network packet processing frameworks, such as PacketShader [31], netmap [32], DPDK [1], etc., mainly rely on the spin-polling mechanism and user-level NIC queues to speed up the processing of packets. Moreover, many modern software data planes, such as Shenango [14], Shinjuku [15], Andromeda [16], IX [12], and ZygOS [13], are all based on the spin-polling mechanism. The characteristic of the spin-polling mechanism is that the CPU cores will _continuously_ and _actively_ check whether new packets arrive. As mentioned earlier, in the software data plane architecture, the CPU core must process packets from multiple Rx queues to which it is bound. Therefore, a CPU core under the spin-polling mechanism polls the Rx queues bound to it in a particular order (e.g., in a round-robin fashion). As shown in Figure 2, the CPU core will check whether there are new packets in each Rx queue in a round-robin fashion. Once the CPU core receives new packets from an Rx queue, it will process them instead of continuing to check the next Rx queue. If these packets are not dropped, they are either put into the Tx queues for forwarding or sent to the controller after processing. Then, the CPU core will continue to check the next Rx queue.  
Typically, CPU cores are polling at high speed on user-level queues, providing faster responsiveness and controllability than traditional IO stacks that rely on interrupt notification capabilities. Although spinning cores design is the key design idea of many low-latency software data planes, it also brings the problem of non-energy saving and wasted processing capability [6]. Under the spin-polling mechanism, the CPU core is always running full-tilt even if there are no packets in the Rx queues. Therefore, there is not always valuable work for the CPU core. Some high-speed data plane processing frameworks provide a hybrid interrupt polling model, such as the l3 fwd-power example provided by DPDK, which aims to solve the problem of non-energy saving at the expense of packet processing capability and latency.
# Microbes as Medicines: Harnessing the Power of Bacteria in Advancing Cancer Treatment
## 2 Mechanism of Tumor Suppression
### Genetically Engineered Bacteria for Anti-Cancer Therapy
#### 2.2.2 Tumor Targeting Enhancement  
Facultative anaerobic bacteria such as _Salmonella_ and _Listeria_ can survive in the oxygenated environment and cause toxicity to normal tissues. Thus, enhancing the tumor targeting of facultative anaerobes is of prime importance. Two approaches can be implemented to potentiate tumor targeting--first, by genetically modifying the bacteria to display tumor specific ligands on its surface. For example, the \(\alpha_{\text{v}}\beta_{3}\) integrin is overexpressed on cancer cells and a _Salmonella_ ppGpp-deficient strain SHJ2037 was designed to display integrin binding peptide Arg-Gly-Asp (RGD) onto the bacterial surface to target the cancer cells. The SHJ2037 strain demonstrated high tumor specificity and enhanced anti-tumor activity in MDA-MB-231 breast cancer cells and MDA-MB-435 melanoma xenografts with overexpressed \(\alpha_{\text{v}}\beta_{3}\) integrin [57]. The second approach is by genetically engineering bacteria to target tumor associated antigens. For this purpose, _Salmonella_ strains with surface display of lymphoma associated antigen CD20 were engineered to carry prodrug converting enzyme herpes simplex virus thymidine kinase (HSV-TK). HSV-TK when co-administered with gancicolvir effectively treated mice with human lymphoma xenografts [58].
# Heart Disease Prediction Using Concatenated Hybrid Ensemble Classifiers
## 2 Related Work
In [32], the researchers assessed the effectiveness of an ensemble semi-supervised learning technique in categorizing chest X-rays related to tuberculosis. Through multiple experiments, which are validated by statistical nonparametric tests, this study showcases the algorithm's competence, suggesting that dependable and resilient predictive models can be constructed even when utilizing a limited number of labeled data points alongside a substantial amount of unlabeled data. Similarly, the authors of [33] assess the effectiveness of two ensemble semi-supervised learning methods in the domain of credit scoring. The numerical experiments conducted reveal that the suggested algorithms surpass their individual semi-supervised learning counterparts, indicating that the integration of ensemble techniques within the semi-supervised learning framework can lead to the creation of dependable and resilient prediction models. While studies like those described above have made significant contributions to the field of applying ML to heart disease prediction, they also exhibit certain limitations. Many of them rely on a limited set of classifiers and may not fully exploit the potential of ensemble learning. Moreover, feature selection techniques in some studies lack thoroughness, potentially leading to sub-optimal predictive performance. To address these limitations, the current research work introduces an approach that combines a wide range of base classifiers and employs advanced feature selection techniques, ultimately enhancing the accuracy and robustness of the proposed heart disease prediction model.