esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Healthcare Delivery: A Model for Staffing, Patient Assignment, and Resource Allocation
## 3 Model Mathematical Formulation
### Constraints  
The decisions are subject to the following constraints:  
1. **Staff Availability** : The total number of staff members assigned to each time slot must equal the demand for staff at that time. \[\sum_{i=1}^{N}x_{it}=D_{t}\quad\forall t=1,\ldots,T\]
2. **Maximum Working Hours**: The total number of time slots each staff member can be assigned to is limited by their maximum working hours. \[\sum_{t=1}^{T}x_{it}\leq H_{i}\quad\forall i=1,\ldots,N\]
3. **Patient Scheduling**: Each patient is assigned to exactly one time slot. \[\sum_{t=1}^{T}y_{pt}=1\quad\forall p=1,\ldots,P\]
4. **Resource Allocation**: Each resource is allocated to exactly one time slot. \[\sum_{t=1}^{T}z_{rt}=1\quad\forall r=1,\ldots,R\]
5. **Skill Level**: The skill level of each staff member assigned to serve a patient in a task during a time slot must be at least equal to the minimum skill level required for that task. \[\sum_{i=1}^{N}\sum_{t=1}^{T}x_{it}S_{i}\geq M_{ipkt}\quad\forall i=1,\ldots,N;p= 1,\ldots,P;k=1,\ldots,K;t=1,\ldots,T\]
6. **Overtime**: If a staff member works overtime, the sum of their assigned time slots must exceed their maximum working hours. \[o_{i}\geq\sum_{t=1}^{T}x_{it}-H_{i}\quad\forall i=1,\ldots,N\]  
**Budget Constraint**: The total cost of staff assignments and resource allocations must not exceed the budget constraint. \[\sum_{i=1}^{N}\sum_{l=1}^{T}(W_{it}x_{it}+O_{i}o_{i})+\sum_{r=1}^{R}\sum_{t=1}^{T }D_{rt}z_{rt}\leq B\]
8. **Workload Balance**: The difference in working hours between any two staff members must be within the maximum allowed difference. \[\left|\sum_{t=1}^{T}x_{it}-\sum_{t=1}^{T}x_{it}\right|\leq F_{ij}\quad\forall i,j=1,\ldots,N,i\
eq j\]  
The decision variables \(x_{it},y_{pt},z_{rt},o_{i}\), and \(v_{ip}\) are all binary, i.e., they take values in \(\{0,1\}\).
## 6 Conclusions  
This study aims to prevent the enemy from taking advantage of the low latency and high transmission rate characteristics of LEOs in the battlefield to obtain information advantages and carry out precise strikes against their opposition. In this study, a distributed dynamic interference scenario in the LEO region is built, the jammers are divided into two categories of primary and relative jammers based on airspace characteristics, the interference effectiveness evaluation functions including time, frequency, and power domains is established, and a multi-dimensional interference optimisation method based on hybrid game theory for LEO satellite signals is proposed. Field interference tests are conducted in a large microwave darkroom at the Xi'an Research Institute in China, and the efficiency of the algorithm is verified by comparing it with the GA algorithm, the DP algorithm, and the GD algorithm, taking into account factors such as the BER, the interference coverage, the value of the utility function, and the Nash equilibrium for the set of strategies. The simulation verifies that the proposed algorithm is able to achieve multiple area coverage, while the utility function value and the BER are significantly better than the other three algorithms, which optimises the distributed jamming resource allocation and improves the jamming efficiency and success rate. The method proposed in this paper has certain application value and development prospects in future military and civilian fields. However, the engineering limitation of this method is mainly that the utility function cannot be adjusted in real time  
Figure 14: Mean utility function values under different optimisation methods.  
for a dynamic environment. In the future, we will consider using neural network (e.g., transformer, etc.) tools to achieve adaptive tuning of utility functions to meet the demands of interference in different environments, as well as exploring distributed interference optimisation methods.  
Conceptualisation, C.T. and X.Z.; methodology, C.T.; software, X.Z.; validation, C.T., L.Z. and Y.L. formal analysis, L.Z.; investigation, C.T.; resources, L.Z.; data curation, X.Z.; writing--original draft preparation, X.Z.; writing--review and editing, L.Z.; visualisation, L.Z.; supervision, Z.D.; project administration, Z.D.; funding acquisition, C.T. and L.Z. All authors have read and agreed to the published version of the manuscript.  
The authors would like to thank the editors for their rigorous work and the anonymous reviewers for their comments and suggestions. This work was supported in part by the National Natural Science Foundation of China under Grants 62171375, 62271397, 62173276, 62101458, 62001392, 61803310, and 61801394, and in part by the Shenzhen Science and Technology Innovation Program under Grant JCYJ20220530161615033.  
The raw data supporting the conclusions of this article will be made available by the authors on request. The data are not publicly available due to future experiments require the use of collected data.  
The authors declare no conflicts of interest.
## 4 Application Features
### Components
#### 4.2.1 Import Meal  
In a food plan, the nutritionist defines the meal and it is delivered to the patient or its IC. Every imported meal is dynamic and can be composed of a combination of soup, main dish, water, and desert (Figure 5). Each element contains different food consumed (or not) by the AD patient. These data are stored in a structure containing Boolean values determining the element's visibility in the dish tab.  
Moreover, when a user consumes a dish (c.f. Section 4.2.3), this list provides feedback to the user on the meal consumed. To achieve that, the data structure containing the food in the dish is compared to the food consumed. A checkmark is added to the meal to represent its consumption when there is a complete match.  
Another important feature is the food placement in the dish when the food plan is imported. The D3.js (Data-Driven Documents) [57] provides a set of libraries that allow data to bind to a Document Object Model (DOM). This paper implemented a variation of the Zoomable Circle Packing library [58], where a circle represents each node (meal). All children of that node (food in a meal) are positioned next to each other at that level, as presented in Figure 3 of Section 4.1. This library determines the size and minor circle  
Figure 4: General view of a meal and food placement in a meal.  
Figure 5: Meal import with different meal tabs depending on the prescribed food plan, and feedback on meal consumed using a visual marker.  
spaces considering the amount of food to be ingested (in grams). It allows the correct representation of food for a natural and dynamic visualization of meals to the user, depicted in Figure 6.  
Another important feature is the correct representation of food in a meal when the food plan is imported into the application. Visually, if a certain amount of prescribed food is low, then the visual representation of this food in the dish must be low (as in a normal dish). For example, if broccoli has a low percentage in the dish, its size has to be adjusted to be visually adequate. To implement relative amounts of food in the dish, the flow presented in Figure 7 was applied.  
The logic is implemented in the following manner. First, it is important to consider that a food plan is composed of the amount of food (in grams) that must be consumed to reach the goal of nutrients for each day. Therefore, the amount of food is converted into the percentage of food occupied in a dish. Next, the percentage of food is converted into images representing that food, and a meal is visually generated. For example, if 100 g of a specific food is represented by one image, 200 g is represented by two images of that food.  
Figure 6: View of meals dynamically created using the Zoomable Circle Packing library.  
Figure 7: Logic associated with dish presentation.
## 1 Introduction
This is where knowledge graph technology comes into play. Knowledge graphs provide a structured framework for organizing and integrating diverse sources of information, including regulatory guidelines, historical incident data, and domain-specific expertise [31]. By leveraging semantic relationships and contextual insights, knowledge graphs can enhance the interpretation of complex data, support real-time decision-making, and facilitate the development of adaptive emergency management strategies [32,33]. Therefore, this study innovatively integrates digital twin and knowledge graph technologies to address the challenges of emergency management in urban tunnel construction. By leveraging the real-time monitoring and predictive capabilities of digital twins and combining them with the structured knowledge representation and reasoning abilities of knowledge graphs, this approach aims to enhance risk detection, decision-making, and response coordination. The proposed framework not only bridges the gap between data-driven insights and expert knowledge but also provides a scalable and adaptive solution for managing emergencies in complex urban environments. The remainder of this paper is organized as follows: Section 2 introduces the engineering background, with a particular focus on the challenges and issues encountered in practical urban tunnel construction projects. Section 3 details the proposed methodology, including the integration of digital twin and knowledge graph technologies for emergency management. Section 4 presents the development of the digital twin platform and its practical applications in addressing real-world scenarios. Section 5 discusses the results, implications, and limitations of the proposed approach. Finally, Section 6 concludes this paper by summarizing the key findings and outlining potential directions for future research.
there are also shortcomings to TDM MIMO radar. The delay caused by the switching process of TDM MIMO radar cannot be ignored in the TWI of moving targets. In the signal acquisition of the through-wall moving target, the position of the moving target has changed within the acquisition time of the single-frame echo [10]. There will be high sidelobes and position deviation when the echo of the moving target is used for imaging directly, which is called range migration here [1]. Compensating for range migration in TDM MIMO radar is essential before imaging. Achieving through-wall, real-time, high-precision motion compensation is a major challenge. [PERSON] et al. proposed a random switching strategy, using sparse reconstruction technology to reconstruct the doppler spectrum of the target, to solve the range migration [12]. However, the timing of random switching needs to be realized using a convex optimization algorithm for specific scenarios. In [13], the phase error is reduced by optimizing the switching scheme of the transmitting antenna. In [14], a signal modulation scheme with an alternating emission is designed to compensate the coupling-phase change produced by the velocity. Both methods can eliminate range migration, but the scan rate of the system is reduced. A motion compensation algorithm based on multi-frame data fusion (MFDF) is proposed in [3] for through-wall motion compensation. It constructs a geometric model based on a uniform array, to obtain the position difference of the target. The geometric solution is then used for echo correction. However, this method is only applicable to 2D TWI, and cannot be extended to 3D TWI. For the motion compensation of 3D TWI, we have proposed the single-channel motion compensation algorithm (SCMCA) and the reference channel-based motion compensation algorithm (RCMCA) in our previous work [10]. Two adjacent frame echoes of a single channel in the SCMCA are used to correct the echoes of moving targets, then its accuracy is limited by the switching rate of the switch. A reference channel is installed in the center of the array to obtain the position difference of the target in the RCMCA, which can compensate the MIMO channels accurately. But the extra reference channel will increase the complexity of the hardware design. In this paper, a motion compensation algorithm based on a sparse array design is proposed, to solve the range migration of 3D TWI in TDM MIMO radar. An improved sparse array is designed in the proposed method; the coincident array elements in the equivalent array are used as compensation channels to calculate the motion information of the target, which is used to correct the remaining MIMO channels. The system complexity is not increased at any point during the compensation process. The remainder of this paper is as follows. The model of 3D TWI for moving targets is established in Section 2. Section 3 presents the principle of the RCMCA, and the derivation of the proposed algorithm. Section 4 analyzes the simulation and experimental results in detail. Finally, Section 5 presents the conclusion.
## 3 Proposed Jamming Suppression Method
### Analysis of Performance Degradation Under a Unified Suppression Strategy
The above equation shows that the DLL tracking error mainly depends on the power of the spoofing interference signal, the phase difference between the spoofing signal and the real signal, the autocorrelation characteristics of the spread spectrum code, and the relative code phase deviation between the two signals. The mechanism by which spoofing interference affects the tracking loop is similar to that of multipath effects. When the relative delay is less than approximately 1.5 chips, the interference signal distorts the discrimination function, causing additional tracking errors. However, when the delay exceeds this threshold, the receiver misinterprets the spoofing interference as a real signal and achieves stable locking. Under the assumption that the linear pseudo range observation model holds, the effects of pseudo range disturbances introduced by each satellite on the receiver's position solution can be considered as linear superpositions. A two-satellite spoofing interference scenario was constructed using the Badious system's PRN2 and PRN5 satellites, with the maximum spoofing slope in the negative X direction of the receiver (geometric sensitivity coefficients: PRN2 = \(-\)0.8, PRN5 = \(-\)0.6). In the simulation, the pseudo range extension values of the two satellites were linearly scanned within the range of 0 to 100 m to analyze the impact of different pseudo range extension combinations on positioning errors. The results in Figures 2-6 show that the error in the X direction exhibits an enhanced trend, with a slope of \(-\)1.4. When the pseudo range extensions of both satellites are 100 m, and the positioning error along the X axis reaches 140 m, accounting for 94.7% of the total positioning error. This validates the decisive influence of multi-satellite spoofing interference on the receiver's position error, with the extent of error amplification constrained by the spatial geometric relationship between the satellites and the receiver. Figure 5: Positioning error varies with changes in the extended pseudo range of PRN2 and PRN5.
## 3 The Proposed Scheme
Otherwise, they obtain the other party's public key by removing the decoy photons. _Shared Quantum Key Computation:_  
PD and HP perform the following operations:  
1. Grover Oracle Application: PD and HP apply their respective Grover oracle \(O_{k_{i}^{PD}}\) and \(O_{k_{i}^{HP}}\) to each other's public key. Specifically, they apply \(O_{k_{i}^{PD}}O_{k_{i}^{HP}}O_{f_{i}}|\psi\rangle\) and \(O_{k_{i}^{HP}}O_{k_{i}^{PD}}O_{f_{i}}|\psi\rangle\). 2. Diffusion Operation: PD and HP then apply the Grover diffusion operator \(D\) to the above quantum states, resulting in \(DO_{k_{i}^{PD}}O_{k_{i}^{HP}}O_{f_{i}}|\psi\rangle\) and \(DO_{k_{i}^{HP}}O_{k_{i}^{PD}}O_{f_{i}}|\psi\rangle\). Upon measurement, both parties obtain the same classical value, namely \(k_{i}^{PD}\oplus k_{i}^{HP}\oplus f_{i}\) and \(k_{i}^{HP}\oplus k_{i}^{PD}\oplus f_{i}\), respectively, where \(\oplus\) is a bitwise XOR operation. 3. Shared Key Computation: Based on the property of bitwise XOR oracle composition, both PD and HP compute the shared quantum key as \(k_{PD\_HP}=k_{i}^{PD}\oplus k_{i}^{HP}\oplus f_{i}\) and \(k_{HP\_PD}=k_{i}^{HP}\oplus k_{i}^{PD}\oplus f_{i}\). Since bitwise XOR is commutative and associative, we have \(k_{AB}=k_{BA}\), ensuring both parties derive an identical shared key. After \(n\) rounds of the above procedure, both the Patient's Device and the Healthcare Provider will derive the same shared quantum key \(K=K_{PD}\oplus K_{HP}\oplus f\). Because the Grover oracle is commutative and associative under the property of bitwise XOR oracle composition, both participants will derive the same shared quantum key.
# BLE Signal Reception and Localization Performance with Varying Receiver and Beacon Setups
## 2 BLE Signal Collection Setup
### BLE Beacon Position on the Body  
Five beacons were placed on a male subject, measuring 170 cm in height and weighing 71 kg. The beacons were positioned at the following locations: left shoulder, left chest pocket (top pocket), wrist, lower front pocket (front pocket), and lower back pocket (back pocket). These positions were chosen based on typical behavior, with three of the locations representing common pocket placements where individuals often store small items similar in size to the beacons.  
The wrist beacon was worn on a watch strap. The beacons in the top, front, and back pocket positions were inserted inside the respective pockets. The shoulder beacon was affixed to the fabric of the shirt using adhesive. During the measurement process, only the subject was present in the indoor location, and no smartphones were permitted in the area.  
The design aimed to minimize the invasiveness of the device positions. The wrist position was included due to its common use for wearing watches, making it both visible and natural. Although the shoulder position is less frequently utilized, its potential technical advantage lies in reduced signal interference with the human body, despite being more invasive compared to other locations. Figure 2 illustrates the BLE device positions on the body.  
Figure 2: The considered BLE positions on the body: front pocket, back pocket, top pocket, shoulder, and wrist.
# Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms
## 3 Materials and Methods
### Spectral Band Ratioing  
By utilizing two spectral bands to compute a ratio image (division), band ratios highlight the spectral variations across mineral targets. This eliminates the topographic impact, which is roughly constant across all bands [57]. Bands are selected to draw attention to a specific material's presence [58]. The process of band selection involves examining the target material's spectral signature and selecting a wavelength band where the mineral appears bright due to high reflection and a band where it appears dark due to significant absorption. In order to obtain a higher ratio value (greater than 1.0) and make the target mineral look brighter in the ratio image, the more reflective band is employed as the numerator [59; 60]. To distinguish the changed zones from the host rock, ratios were therefore built using the spectral characteristics of the lithologies. According to [61], adding images tends to accentuate information that is connected between bands and produces a compounded impact. Therefore, band ratios are helpful data reduction strategies that bring attention to the small spectrum differences hidden by the image's brightness variations.
## 4 Results and Discussion
### Problem Statement  
The 2D micromodel is shown in Figure 2, and its porosity is 0.33. The entire micromodel is divided into \(L\ \times\ H=2644\times 2117\) lu\({}^{2}\) with a resolution of 0.67 \(\upmu\)m per lu. The narrowest throat has a width of 12 lu which is fine enough to produce grid-independent results in two-phase displacement simulations [7].Twenty-two extra layers of lattices are added to both the inlet and outlet to facilitate the implementation of boundary conditions, so the actual domain size is \(2688\times 2117\) lu\({}^{2}\). Here, the flow velocity for each fluid is so small that the inertial effect becomes insignificant.  
The constant pressure boundary condition developed by [PERSON] and [PERSON] [40] was imposed on the inlet and outlet, which are located at the left and right sides of the domain, respectively. The halfway bounce-back scheme and wetting boundary condition were applied at the walls that contain the surfaces of solid grains as well as the upper and lower sides of the domain. The densities of both wetting and non-wetting fluids were set to be unity as the density difference between oil and water is insignificant. The dynamic viscosity of the displaced fluid was kept as 0.167 mu\(\cdot\)lu\({}^{-1}\cdot\)ts\({}^{-1}\), and the dynamic viscosity of the displacing fluid was varied to achieve different viscosity ratio values (\(\lambda=\mu_{displaced}/\mu_{inreading}\)). Initially, for \(x\geq 22\) lu, the domain was filled with the displaced fluid (represented in red) at rest, and a constant pressure \(p_{out}=1/3\) mu\(\cdot\)lu\({}^{-1}\cdot\)ts\({}^{-2}\) was imposed at the outlet, while for \(x<22\) lu, the region was occupied by the displacing fluid (represented in blue) with a higher pressure at the inlet. The simulations stopped when the displacing fluid broke through at the outlet to avoid the capillary end effect [43].  
By studying the dependency of porosity on the size of the rock sample, we calculated the porosity using different sizes of Berea sandstone to find its representative elementary volume (REV). The results are shown in Figure 3, which demonstrates that the chosen size of \(2688\times 2117\) lu\({}^{2}\) could be a good REV of the Berea sandstone. This guarantees that the simulated geometry included a sufficient number of pores for the meaningful statistical average, which is generally required by the continuum concept [4].  
Figure 2: The 2D pore network used in the present lattice Boltzmann method (LBM) simulations. The solid grains are represented in gray, while the displacing and displaced fluids are represented in blue and red, respectively. The domain size is 1774 \(\upmu\)m by 1418 \(\upmu\)m.
## 2 Materials and Methods
### Selection Criteria  
Once we had compiled the preliminary list of potential studies, all titles were reviewed to eliminate any duplications. In the next step, we filtered the titles and subsequently examined abstracts to identify and exclude irrelevant articles. This included those not directly related to the topic under review, as well as non-observational studies, such as reviews, case reports, and letters to the editor. To ensure we did not exclude any valid study to our research, all the abstracts were evaluated and validated by five authors. At the end, records were again filtered with a complete reading of the full text of the studies that remained after exclusion.  
This meta-analysis had the following inclusion criteria:  
* Primary diagnostic prospective or retrospective studies evaluating deep learning models for the diagnosis of adnexal masses and comparing them to expert evaluation.
* Collected Data allows the construction of a 2 \(\times\) 2 table to estimate true positive, true negative, false positive, and false negative cases for any of the index tests assessed.
* The reference test was considered histological confirmation.  
The exclusion criteria were the following:  
* Studies not related to the topic.
* Studies evaluating AI or ML other than DL.
* Secondary studies (other meta-analysis or systematic reviews) or those where insufficient data were provided.
* Studies where other imaging studies such as MRI or CT were considered.
* Articles that did not compare deep learning results with those obtained by expert examiners.
* Studies where histological confirmation was not the gold standard or was not available.
* Studies combining evaluation of biomarkers to ultrasonography.  
We used the snowball strategy to identify potentially interesting papers by reading the reference lists of the papers selected for full text reading.