esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This study presents the new Concurrent or Ordered Matrix-based Packing Arrangement Computation Technique (COMPACT). The proposed method relies on approximating the object shapes by matrix-based (raster) representations. Such an approach provides versatility for packing various shapes and allows the use of new performance metrics.  
In the proposed framework, the raster representations are obtained through loop-free operations that improve efficiency. The objects can be rotated by arbitrary angles, different from the right-angled rotation restrictions imposed in many existing packing optimization studies based on raster methods. Besides, a novel performance metric is introduced. This metric facilitates efficient filling of the available space by maximizing the overall contact within the domain. Moreover, exploitation of the objective functions for discarding overlap and overflow constraints is explored to enable the use of unconstrained optimization methods.  
As case studies, the developed technique was utilized to pack multiple rectangular and circular objects in square bins. The suitability of concurrent and ordered placement strategies for solving different problems was investigated. In addition, the effectiveness of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Rectangle** & & **Circle** \\ \hline
**Quantity** & **Height** & **Width** & **Quantity** & **Diameter** \\ \hline
4 & 40 & 40 & 2 & 19 \\
1 & 28 & 28 & 4 & 11 \\
4 & 20 & 15 & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Quantities and dimensions of the rectangular and circular objects used in the third study involving rotational degrees of freedom.  
Figure 9: A sample optimal solution for the objects used in the third study involving rotational degrees of freedom. The filling ratio for this packing configuration is 92.3%.  
the utilized performance metrics was analyzed for various scenarios. The results showed that the proposed technique performs effectively in obtaining plausible packing configurations. The case studies also provide guidelines for the selection of suitable packing strategies, performance metrics, and constraints according to the problem type.  
The developed techniques can certainly be extended for three-dimensional objects in future studies. They can also be used for packing problems involving various other objects and domain geometries. In particular, the new performance metric relying on contact maximization offers great potential for packing non-convex polygons. Although the genetic algorithms perform well with the described formulations, the implementation of other nonlinear optimization methods into the developed framework is also possible.  
This research received no external funding. The article processing charge was covered by the Max Planck Digital Library.  
Not applicable.  
The data presented in this study are available on request from the author.  
The author declares no conflict of interest.  
The following abbreviations are used in this manuscript:  
A1 Adjacent ones  
DBL Distance to the bottom-left  
DC Distance from the center  
T1 Total ones
## 5 Possibilities of Reducing the Acrylamide Content in French Fries and Potato Chips
### Technological Factors
#### 5.2.3 Pre-Drying Process  
Another thermal stage used in the technological production of French fries (and potato chips after frying) is pre-drying. The main purpose of this process is to remove excess water from the surface of the semi-finished product before frying. A lower moisture content in the cuttings allows for shorter frying times and, as a result, less fat in the French fries [3; 4; 12; 26; 27; 129], and, most importantly, reduces the acrylamide content in the ready product [49]. There is a relationship between water content and water activity (aw) in the product and the aspect of acrylamide formation. It was found that acrylamide is formed with a water activity ranging between 0.4 and 0.8, while the amount of acrylamide in the ready product increases when the moisture content is below 5% [49]. Pre-drying can be carried out at different times and temperatures, along with the possibility of using the convection, microwave, and microwave-vacuum methods [129; 130].
# Hybrid A-Star Path Planning Method Based on Hierarchical Clustering and Trichotomy
## 2 Single Linkage Hierarchical Clustering Algorithm  
### Scene Map Preprocessing  
The research was conducted based on a known map since the algorithm proposed is a global path planning algorithm, and the existing research on global path planning algorithms was based on known maps; even in different known maps, the algorithm can run without limitations. In a known map, the distribution of obstacles, as well as the shape, size, and position of obstacles, are known in advance. Hierarchical clustering calculates the distance between data points of each category and all data points to determine their similarity. The smaller the distance, the higher the similarity, and the two data points closest to each other are combined to generate a clustering tree. To generate data points, it is necessary to use the Prewitt operator to identify the boundaries of obstacles, and only after the identified obstacles are discretized can data points be obtained. The principle of the Prewitt operator is to realize edge detection by using the difference generated by the gray value of pixels in a specific region. Although the map is known, there is no guarantee that the map is grayscale. During processing, it needs to be processed into a grayscale map to facilitate the work of the Prewitt operator. Firstly, the scene map is converted into a grayscale image. Then, the Prewitt operator is applied for edge detection of obstacles. The Prewitt operator is a type of differential operator used for image edge detection, which relies on the difference in pixel grayscale values within a specific region for edge detection [25]. It is suitable for identifying images with high noise and grayscale gradients, as it is a first-order differential operator [26]. The Prewitt operator employs a \(3\times 3\) template and utilizes the grayscale differences between the pixel's upper, lower, left, and right neighboring points for edge detection. Therefore, its edge detection results are more pronounced in both the horizontal and vertical directions compared to the Robert operator. The Prewitt operator's mathematical expressions are as follows:  
\[G(x) =[f(x-1,y+1)+f(x-1,y)+f(x-1,y-1)]-[f(x+1,y+1)+f(x+1,y)+f(x+1,y-  
Figure 1 provides calculation templates for the horizontal and vertical directions of the Prewitt operator. The value in each cell in the template represents the pixel value corresponding to that position. The Prewitt operator extracts the edge by calculating the gray difference in the horizontal direction and vertical direction. Figure 1a is the horizontal direction of the calculation template: \(G(x)\), Figure 1b is the vertical direction of the calculation template: \(G(y)\). After edge detection is completed, the map undergoes binarization, where the values of obstacle positions are set to 1, and the remaining positions are set to 0. Finally, the boundaries of the obstacles are discretized into several points, preparing for obstacle clustering.
## 3 Materials and Methods
### Energy Management System
#### 3.1.1 DC-Link Voltage Loop  
Considering the \(V_{DC}\) loop at first, its task is ensuring that the DC-link voltage is held constant at the desired value. Therefore, considering the current balance at the DC-link (Figure 1), the following relationship holds:  
\[C\frac{dV_{DC}}{dt}=i_{m}-i_{g} \tag{1}\]  
where \(C\) is the capacitance of the DC-link capacitor module, \(V_{DC}\) is the DC-link voltage, while \(i_{g}\) and \(i_{m}\) denote the current delivered by the DC-link to the grid and by the M/G to the DC-link, respectively. By multiplying both sides of (1) by \(V_{DC}\), the power balance can be achieved easily as  
\[\frac{dE_{DC}}{dt}=P_{DC}\,\ E_{DC}=\frac{1}{2}C\ V_{DC}^{2}\,\ P_{DC}=P_{m}-P_{g}=V_{DC} \big{(}i_{m}-i_{g}\big{)}. \tag{2}\]  
Therefore, since \(V_{DC}\) and \(E_{DC}\) are proportional to each other, DC-link voltage regulation can be assured by regulating \(E_{DC}\) through \(P_{DC}\), leading to the DC-link voltage loop structure shown in Figure 4a. In particular, a Proportional-Integral (PI) regulator is employed, whose gains have been synthesized in accordance with (2) and the desired closed-loop dynamic performance. Power limitation and anti-windup filter have been considered as well to be compliant with system constraints. Once \(P_{DC}^{*}\) has been determined, its tracking is demanded either to FESS-based or to grid-based EMS depending on the FESS operating modes resumed in Table 1.  
Figure 3: The EMS equivalent block scheme proposed for the FESS.
## 5 Conclusions and Recommendations  
In conclusion, it is feasible to establish and implement an AMS program in a low-income country such as Sierra Leone, where hospitalized patients are likely to be given antibiotics.  
We recommend the following in establishing an AMS program in a low-income country:  
1. Use existing structures, such as IPC committee and teams (where available).
2. Establish partnership with local and international agencies.
3. Secure funding.
4. Establish the policy environment such as terms of reference for the AMS team.
5. Establish a multidisciplinary AMS team.
6. Train healthcare workers.
7. Create an action plan to guide activities.
8. Use pre-existing GPPS methodologies to track progress of the AMS work.  
Conceptualization, S.L., S.T., H.L., A.J., C.B. and M.B.; methodology, S.T., S.L., H.L., N.S., I.J., C.T. and S.B. Formal analysis, E.F., S.L. and D.S.; data curation, N.S., N.K.N., I.J., C.T., S.T., S.B. and D.S.; writing--original draft preparation, S.L., S.T., M.N.P.B., H.L., G.F.D., M.S.K. and J.B.W.R. writing--review and editing, S.L., S.T., M.B., E.F., A.J. and S.B.; funding acquisition, C.B., M.S.K., M.B., G.F.D. and J.B.W.R. All authors have read and agreed to the published version of the manuscript.  
This project is funded by the UK Department of Health and Social Care's Fleming Fund using UK aid. The views expressed in this publication are those of the authors and not necessarily those of the UK Department of Health and Social Care.  
Ethics approval was obtained from the Sierra Leone Ethics and Scientific Review Committee of the Ministry of Health and Sanitation, Government of Sierra Leone.  
This is an observational study in the context of implementing a WHO-recommended program to reduce inappropriate antimicrobial prescribing at institutional level. The study procedures do not involve risk to the participating institution and therefore informed written consent was not necessary for this study. Permission to collect the data was obtained from the administrations of Connaught Hospital.  
Data Availability StatementData is available at the University of Sierra Leone repository and will be available on request.  
The GPPS was supported by the AMS Pharmacist Champions; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]. The project also received support from the THET Consultant, [PERSON], and the National AMR Program Focal, [PERSON].  
The authors declare no conflict of interest.
# Optimal Model for Carsharing Station Location Based on Multi-Factor Constraints
## 3 Electric Carsharing Location Optimization Model
### Mathematical Model
#### 3.3.1 Objective Function  
Many factors should be considered in the station location of electric carsharing. The most important factor is user demand. The more user demands that are satisfied, the larger the economic benefits. This factor is the foundation and value of existence of the electric carsharing company. Therefore, the objective function is to maximize the user demands that are met in accordance with Equation (7).  
\[\text{max}S=\sum_{u=1}^{m}\sum_{v=1}^{n}\min(P_{uv},R_{uv}), \tag{7}\]  
where \(P_{uv}\) is the service provided by the stations in row \(u\) and column \(v\). \(R_{uv}\) is the demand of the grid in row \(u\) and column \(v\). S is the total number of user demands that can be met.
## 5 Addressing the Research Questions
### RQ1: What Are the Primary Fields of Application Where VR, AI, and IoT Are Used in Combination?
#### 5.2.1 Healthcare  
In healthcare, the combined use of VR, AI, and IoT addresses critical challenges in patient care, diagnostics, and especially medical professionals' training. IoT devices enable real-time monitoring of patient vitals (e.g. [65]) and environmental conditions while AI algorithms provide predictive analytics for early diagnosis and treatment planning as well as info related to procedures, meanwhile VR creates immersive simulations for medical training and rehabilitation. DT frameworks are widely adopted and with the integration of IoT sensors to continuously gather patient data, AI models to predict health anomalies, and VR environments to simulate surgical procedures, thus enhancing both preventive care and medical education as seen in [68]. The collaboration offers realism, reduces errors, improves efficiency, and facilitates personalized healthcare, that wouldn't be possible if one of those key technologies were missing. In addition to DTs many assistive device, such as in [66], are being created utilizing sensors, AI and many times VR or AR in order to improve people's daily life with health problems such as the cognitive impaired.
###### Abstract  
Simply speaking, automatic driving requires the calculation of a large amount of traffic data and, finally, the obtainment of the optimal driving route and speed. However, the key technical difficulty is the obtainment of data; thus, radar has become an indispensable hardware for automatic driving. Compared to the optical and infrared radar, millimeter-wave radar is not affected by the shape and color of the target, and it is not affected by the atmospheric turbulence, compared to ultrasonic, and so it has a stable detection performance and good environmental adaptability. It is little affected by changes in the weather, and the external environment, rain, snow, dust, and sunshine have no interference in it. The Doppler frequency shift is large, and the accuracy of the relative velocity measurement is improved. However, one challenge for vehicles in fast environments is millimeter-wave-based communication. Because of the short wavelength of the millimeter wave and the high path and penetration losses, the beamforming technology of a large-scale antenna array plays a key role in the construction and maintenance of millimeter-wave communication links. Millimeter waves have wide channel bandwidths, unique channel characteristics, and hardware limitations, and so there are many challenges in the direct use of beamforming technology in millimeter-wave communication. Traditional beam training cannot meet the requirements of low overhead and low delay. This paper, in order to obtain beam information, introduces the context-awareness module to the deep-learning net, which is derived from past observation data. This paper establishes a model that contains the receiver and the surrounding vehicles to perceive the environment. Then, a long short-term memory (LSTM) neural network is used to foresee the acquired power, which is quantized by several beam powers. According to the conclusion, the prediction accuracy is greatly increased, and the model could yield throughput with almost zero overhead and little performance loss.  
m +
Footnote †: journal: Applied Sciences
# Communication-Efficient Vertical Federated Learning
## 2 Background
### Communication-Efficient Federated Learning
Similarly, quantization can be performed on the global model before broadcasting it to the clients for local updates. The Lossy FL algorithm (LFL) introduced in [18] quantizes the global model before it broadcasts and shares it across all the devices. Ref. [19] proposes an improved sign gradient descent [17] to replace conventional gradient descent in FL, which maintains the privacy of model parameters, while significantly decreasing the communication resource consumption. Gradient sparsification [20] is another compression technique that enforces transmitting \(n\) out of \(d\) elements at iteration \(k\), where \(d\) is the total number of elements in the gradient vector and \(n\) is the number of the most important elements to send at iteration \(k\). The work in [21] proposes the sending of only gradients larger than a pre-defined constant threshold. However, determining this threshold is a challenging task for gradient sparsification. The techniques Top-k AllReduce [22] and Deep Gradient Compression [20] were proposed to further improve compression efficiency. Gradient sparsification has also been proven to reduce the overall training time in FL [23] by adaptive selecting of the number of gradients or model parameters. [PERSON] et al. [9] proposed a sparse ternary compression (STC), an extension of the existing compression technique of top-k gradient sparsification [22] which was proven to outperform traditional federated averaging approach in case of bandwidth-constrained learning environments. However, experimental results demonstrating the effectiveness of most of these approaches in VFL setting were not observed. [PERSON] et al. [24] proposed a Federated Stochastic Block Coordinate Descent (FedBCD) algorithm for vertically partitioned data, in which each party conducts multiple local updates before each communication to effectively reduce the number of communication rounds among clients. Furthermore, [PERSON] et al. [25] developed an asynchronous stochastic quasi-Newton (AsySQN) framework for VFL, which performs descent steps scaled by approximate Hessian information, which converges much faster than Stochastic Gradient Descent (SGD)-based methods in practice, allowing for a significant reduction in the number of communication rounds. In [26], an asynchronous vertical federated learning framework with gradient prediction and double-end sparse compression is proposed, where the compression occurs at the local models to reduce training time as well as transmission cost. The existing compression techniques in FL are focused on gradient compression, which reduces training time and transmission costs, but still requires a sufficient number of communication rounds. On the other hand, in this paper, we propose a method based on the data compression technique that compresses local data of clients before aggregating it for final model training. Since no local data are exposed, privacy is protected, and the entire process is limited to a single communication round.
## 3 Ambiguity Suppression Method
### Ambiguity Suppression  
The phase information of the input data should stay complete and not damaged to keep the integrity of main area signals, which makes it necessary to the use of amplitude suppression for ambiguity suppression. We divided the image result acquired in Section 3.1 into two parts according to the detection result:  
\[s_{im}(x,y)=\sum_{i,j\in C_{re}}s_{im}(i,j)+\sum_{p,q\in C_{de}}s_{im}(p,q) \tag{23}\]  
where \(C_{de}\) represents the collection of detected pixel positions and \(C_{re}\) represents the collection of remaining pixel positions.  
The amplitude of detected pixel is reduced by \(N\) times, and we can get the result as:  
\[s_{pr}(x,y)=\sum_{i,j\in C_{re}}s_{im}(i,j)+\sum_{p,q\in C_{de}}s_{im}(p,q)/N \tag{24}\]  
The extent of suppression is alternative for manually input. Generally, we choose the attenuation multiplier \(N\) for the aim that ambiguous signal power lower than main area power after standard imaging by 0-5 dB.
## 3 Deep Learning Models and Methods
### Markov Model  
Markov models are statistical techniques that forecast the position of an element in the sequence by analyzing the past based on early occurrences. Markov models predict the next word by assuming that the likelihood of the word relies only on the preceding few words and not on the complete historical text. The next state is governed by only the current state. This can be modeled using Markov chains, and it is an ideal method for predicting a sequence of events. A Markov chain is a mathematical model of a series of trials in which the possibility of an individual event depends on the state obtained by the prior event. This model consists of different states, transitions, and transition matrix. For word prediction in a Markov chain, every word in the passage is a state.  
Transition probabilities show the chance of changing from one word to another. The frequency with which one word follows the other in the provided text is based on calculating the probabilities. The number of times each word comes after another is counted, and the total number of occurrences of the word before it is used to normalize the counts to determine the transition probabilities. The Markov chain chooses the word with the highest transition probability from the current state to predict the word that will come after a given word. The following steps are used in finding the next word using a Markov model. Preprocessing the data includes removing the punctuation, adding to the dictionary, and listing with probability. The next step is training the model, normalizing the distributions, getting the predicted word based on the model, and calculating the testing and training accuracy.  
\[P(X_{n}=i_{n}|X_{n-1}=i_{n-1})=P(X_{n}=i_{n}|X_{0}=i_{0},X_{1}=i_{1},..,X_{n-1 }=i_{n-1}) \tag{7}\]  
P(...) shows the likelihood.  
The random variable at time step n is denoted by X_n.  
i_n depicts the random variable's precise value or state at time step n.  
i denotes probability with conditions.  
X(n - 1) = i_(n - 1): since this is the conditioning event, the probability is computed concerning the prior state, which was i_(n - 1).  
The Markov property is expressed by this equation, which says that the probability of the current state (X_(n - 1), and not of the previous states, determines the probability of the next state (X_n).  
Features of the Markov Model  
Arabic text is generated statistically using Markov chain models, and, to determine the likelihood of word sequences, they examine a sizable text collection. Markov technology generates logical sentences by predicting the most likely next word based on the preceding words. The accuracy and fluency of the model can be increased by using sophisticated methods like smoothing and higher-order n-grams. Long-range dependencies are difficult for Markov chain models to capture.