esa-sceva/satcom-chunk-collection huggingface dataset:
# A Line Segment Detector for Space Target Images Robust to Complex Illumination
## 1 Introduction
In terms of the line segment fitting method, [PERSON] et al. [39; 40] proposed a length-based line segment detector (LB-LSD), which does not use the traditional least squares error to fit the line segments but fits the line segments according to the length condition. Recently, [PERSON] et al. [41] proposed a line segment detection method (AG3 lines) that combines active grouping and geometric gradients. The method consists of three stages: anchor map extraction, active grouping, and line segment validation, which can control false positives and run faster. However, the robustness of the method is not good under complex illumination and noise interference. The main reason is that complex illumination and noise affect the anchor map extraction, which leads to a low number of anchors and extraction errors. The subsequent line segment validation is also affected. In the line segment validation phase, AG3 lines adopts two methods to control false positives from rough to fine. Firstly, the anchor density along the line segment is exploited to control false positives, and then the distribution of the gradient magnitudes along the line segment is used to further control false positives. Due to the error in the anchor map extraction, the anchor density of the line segment changes, and the reduction in the anchors may lead to a situation that the adjacent small line segments from the same line segment fail to connect. The line segment fracture phenomenon caused by the reduced number of anchors when the illumination varies dramatically is shown in Figure 1. Figure 1a is the original image with the presence of two line segments, which should generate Figure 1b after line segment detection. Due to the too-dark illumination conditions, AG3 lines has the problem of decreasing anchors, as shown in Figure 1c. False positives occur after active grouping, and the results are as shown in Figure 1d. The fracture of the line segment causes the adjacent small segments of the same line segment to fail to connect. To furtherly improve the performance of the space target line segment detector, this paper proposes a line segment detection method for space targets under complex illumination named ST_LSD. The noise is removed by introducing adaptive bilateral filtering. The anchor map extraction method is optimized by combining the improved Otsu [42]. An aggregation method is introduced to produce line segment results of higher quality. The remainder of this paper is organized as follows. Section 2 describes the design details of ST_LSD. Section 2.1 introduces the overview of ST_LSD. Section 2.2 describes the noise removal method combined with adaptive bilateral filtering. Section 2.3 details the anchor map extraction method combined with improved Otsu. Section 2.4 discusses the improved line segment validation and the aggregation method. Section 3 provides our quantitative and qualitative evaluation of the proposed method. Section 4 discusses the parameter settings and algorithm complexity of ST_LSD. Section 5 draws the conclusions.
# Dispensing of Prescribed Medicines in Swiss Community Pharmacies-Observed Counselling Activities
###### Abstract  
Background:Patient counselling and addressing drug-related problems are the pharmacist's key activities to ensure the safe and effective use of medicines. This study aimed to describe the dispensing practice of prescribed medicines in daily community pharmacy practice and to identify factors influencing counselling provision; **Methods:** An observational study was conducted in community pharmacies in Basel, Switzerland. One master student in pharmacy performed non-participatory observations for one day at each of the participating community pharmacies. Patient characteristics, counselling content, additional activities, and pharmaceutical interventions were documented on a structured checklist; **Results:** 556 prescription encounters (PE) in 18 participating community pharmacies were observed (269 first prescriptions; 287 refill prescriptions). Patients were regular customers (n = 523, 94.1%) and 53.8 \(\pm\) 23.4 years old. Counselling was provided to 367 (66.0%) customers on 2.9 \(\pm\) 3.1 themes per PE. Factors influencing counselling were dispensing by the pharmacist, new customer, customer who did not refuse counselling, customer with a first prescription, with a prescription resulting in a pharmaceutical intervention, and a prescription filled by carers. During 144 PEs, 203 interventions were documented. Pharmacists proposed few additional activities and performed no cognitive pharmaceutical service; **Conclusions:** Our study quantified counselling and additional services at the dispensing of prescribed medicines and identified influencing factors on counselling provision at the patient, prescription, and pharmacy level.  
community pharmacy practice; dispensing; counselling; pharmaceutical intervention; pharmaceutical care; observation +
Footnote †: journal: _pharmacy_
# Shuffle Model of Differential Privacy: Numerical Composition for Federated Learning
## 8 Experiments
### Federated Deep Learning Simulation  
We assume a general deep learning procedure in the shuffle model, where there are \(K\) iterative gradient aggregation rounds, and each round collects a gradient vector from \(n\) users. Assuming the local budget as \(\epsilon_{0}\), according to optimal numerical vector aggregation protocol [19], each user will send \(s=\lceil\epsilon_{0}\rceil\) bits in each message. Each bit is sanitized using a randomized response with flipping probability \(1/(\exp(1)+1)\). Overall, the procedure is equivalent to \(K\) rounds of shuffle protocols, each using sampled vector \(\{0,1\}\)-randomized response as the local randomizer with \(n\) users. In order to cover extensive scenarios, we vary the number of iterations \(T\) from \(2\) to \(256\), vary the local budget \(\epsilon_{0}\) in \(\{2,4\}\), and vary the number of users from \(10^{4}\) to \(10^{5}\).  
The work of [13] for privacy composition of single-message LDP shuffling protocols is applicable to such a scenario, as the sampled vector \(\{0,1\}\)-randomized response with flipping probability \(1/(\exp(\epsilon_{0}/s)+1)\) satisfies \(\epsilon_{0}\)-LDP. We compare our proposal in Theorem 1 with the numerical approach in [13], which had the tightest so far composition bounds (compared to the Renyi DP-based approaches in [8; 10]). We present the experimental results in Figure 2. Since our proposal in Theorem 1 provides a provably tighter dominating pair for vector \(\{0,1\}\)-randomized response, it consistently reduces about \(20\%\) privacy consumption when compared to [13].
## 2 Materials and Methods
### Operation and Stabilization  
The two pump diodes, each operating at an average power of about 1.8 W, keep the laser operating just above lasing threshold at all times. This CW signal is monitored by reflecting a fraction of the light from the surface of the etalon to an amplified photodiode. Dithering of the length of the etalon by applying a sinusoidal modulation voltage and lock-in detection of the induced intensity fluctuations generates an error signal. This signal is proportional to the frequency mismatch between laser cavity mode and etalon mode and can be used to synchronize the etalon to changes of the cavity mode, thus preventing mode-hops. The narrowband CW Ti:sa laser light is then amplified after the Nd:YAG pump pulse is applied. While the pulsed pump source introduced a considerable source of noise to the error signal used for wavelength stabilization, suitable conditions for stable operation and wavelength scanning were determined.  
Control of the Piezo actuator voltages, as well as generation of modulation signal, filtering and PID control is done using a commercial servo controller (TEM Laselock), which features two independent regulators. Internally, the Laselock first digitizes the photodiode signal, mixes it with the oscillating dither voltage and performs low-pass filtering to generate the error signal. This error signal is then amplified according to the PID controller settings and fed back to the HV output controlling the etalon piezo.
# Tie-System Calibration for the Experimental Setup of Large Deployable Reflectors
## 3 Results
explained by considering that the deformation of the truss support relaxes the front and rear tension truss systems, making the tension distribution more uniform. Moreover, other simulations with different initial RMS errors revealed that, with the given design data, the maximum initial RMS error which can be fixed is about 1 mm. Beyond this limiting value, a revision of the design data is needed. This demonstrates the validity of the method only for small RMS errors.
## 6 Implications  
### Theoretical Implications  
Our findings contribute to the theory in different ways. First, by comprehensively defining definitions of situational information that will be useful for researchers and also enrich the existing literature, even though some prior studies have paid attention to disaster-related information in a healthcare context, but these studies focused on one or two aspects of information while neglecting the other aspects of situational information [13]. So, our study expanded the prior research and examined the each aspect of information during the healthcare crisis with more fine-grained analysis, and put it into situational information types. Different studies approached natural disasters by analyzing only emotional response [56], or just investigating the help/aid information [57], and evidence-based disaster information [17]. However, we suggest the characterization of every type of COVID-19 pandemic situational information along with word and linguistic patterns that people use in their tweets; such linguistic patterns uncover the information needs and sentiments of people. These linguistic patterns imply the need to utilize different types of information sharing strategies for different types of situational information. The observed descriptive findings of this research imply that this framework can be applied in different contexts of natural disasters and for crisis response strategies. Some notable empirical findings are: type 1 (announcements or measures) has largest amount of verified users involved in the spreading of this type of information, which confirms the validity and authenticity of this type of information, especially in COVID-19 pandemic situation, while type 4 (help seeking) information attracted the largest numbers of retweets which indicate people want to help each other by sharing more of this information type.
# A Review of Research on Secure Aggregation for Federated Learning
## 2 Federated Learning
### Classification of Federated Learning
#### 2.2.2 Client Size  
Based on the size of the number of clients, federated learning can be categorized into two types--cross-device federated learning and cross-silo federated learning. Cross-device federated learning refers to federated learning involving a large number of widely dispersed devices. Cross-silo federated learning involves a small number of organizations that store large amounts of sensitive data.  
Cross-device federated learning is a federated learning approach trained using a large number of clients that have similar domains to the global model, such as IoT-based systems [21], where each device independently collects its usage data. The advantage of this method is that it maximizes the use of decentralized data sources while protecting user privacy.  
Cross-silo federated learning typically involves large organizations or companies as clients, where different companies collaborate to build a federated system, usually ranging from as few as 2 to 100 devices, where each company maintains local data and performs model training based on its respective database. Cross-silo federated learning is more flexible in its implementation and can be trained on the data as needed by the organization.
## 6 Simulation Results
### Accuracy of the Extracted Feature Vectors  
Before testing the robustness of our scheme against different types of attacks, we validated the accuracy of the extracted feature vectors, as these are the main elements designed to resist signal-processing attacks. For this purpose, we used a set of biomedical  
Figure 14: The hardware design of Raspberry Pi 4 model B that was used in our work.  
signals selected from the database in [58] in the rest of our work. Next, the selected signals were normalized to the range.  
To conduct the present test, we used three test bio-signals: the first one is an electroencephal-cephalography (EEG) signal frame labeled \"chb01_01_edfm\" with size L = 1024 (Figure 15a). The second is a frame of ECG signal labeled \"Record_108\" of size L = 4096 (Figure 15b), and the third frame is an electroagstrogram (EHG) signal frame labeled \"tpehg1007\" of size L = 6400 (Figure 15c). Next, we used (2) to calculate the 2 DHMs of the test frames. Then, (3) was used to obtain the reconstructed signal frames. Finally, to quantify the reconstruction error between the original signal frames and their reconstructed versions, we used the MSE criterion defined by (4). The results of this test are shown in Figure 15. This figure shows that the MSE values tended to be zero for the three reconstructed signal frames, demonstrating the high precision of 2 DHMs. This represents the bio-signal feature vectors in our work. This result confirmed that the extracted feature vectors are stable and accurate.  
It is worth mentioning that the optimal parameters of 2 DHMs were obtained by using the method presented by [PERSON] et al. in [23], which is based on the sine cosine algorithm (SCA) [24].
# Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning
## 1 Introduction
According to [PERSON] and [PERSON] [8], MR technology has emerged as a powerful tool in medical environments, providing real-time interaction and contextual visualization that surpasses traditional training and operational methods. Their study highlights the growing adoption of MR for medical training, improving procedural accuracy and decision making. However, while MR has been widely explored in pediatric healthcare and training, its application in real-time pharmaceutical environments, such as medical vial identification, remains underexplored. Recent research [9] highlights the use of extended reality (XR) frameworks like home-based mirror therapy for phantom limb pain, showing the growing relevance of AI-assisted XR interfaces across diverse user groups, including patients, nurses, and physicians. Additionally, the integration of artificial intelligence (AI) and deep learning in healthcare has demonstrated remarkable advancements in areas such as diagnostic imaging, robotic surgery, and predictive analytics. Object detection algorithms, particularly YOLO-based models, have shown exceptional performance in real-time medical applications, enabling the rapid and accurate identification of objects in complex environments [10]. The application of deep learning to vial identification, combined with the use of MR for real-time visualization, presents a novel solution to improve accuracy and operational efficiency in pharmaceutical and medical settings. In our previous research [11], we introduced a barcode-based medical vial detection system that utilizes mixed reality technology, specifically designed for HoloLens 2. However, a notable limitation of this system was HoloLens 2's inability to accurately detect smaller barcodes, particularly those on amiodron and fentanyl citrate vials. To address this challenge, the current study proposes the integration of deep learning models for real-time vial detection using visual attributes for identification rather than relying solely on barcodes. This enhancement aims to significantly improve detection accuracy, especially in environments where barcodes can be obscured or difficult to scan, as well as to reduce the workload of medical professionals by automating the entire treatment process. The study highlights the potential of object detection algorithms to improve system performance and reliability in real-world medical settings.
# A PSO-Based Uneven Dynamic Clustering Multi-Hop Routing Protocol for Wireless Sensor Networks
## 2 Related Work
### Swarm Intelligence Based Routing Protocols
The PSO-based approach was implemented within clusters rather than the BS, which makes it a Particle Swarm Optimization Semi-Distributed method (PSO-SD). However, the distance between nodes and the BS are not taken into account in the protocol, which may result in excessive energy consumption of the CHs to transmit data to the BS.
## 2 Materials and Methods
### Vegetation Productivity and Index Data  
The net (NPP) and gross (GPP) primary productivity of vegetation cover was estimated using the MODIS MOD17A3 HGF product [27]. These data represent raster composites of NPP and GPP values (kgC/m\({}^{2}\)) with a spatial resolution of 500 m ([[https://lpdaac.usgs.gov/products/mod17a3](https://lpdaac.usgs.gov/products/mod17a3) hgfv006]([https://lpdaac.usgs.gov/products/mod17a3](https://lpdaac.usgs.gov/products/mod17a3) hgfv006) (accessed on 20 February 2025)). The GPP and NPP estimations are based on the following equations [27]:  
\[GPP_{annual}=\sum_{year}GPP_{daily} \tag{1}\]  
where \(GPP_{annual}\) and \(GPP_{daily}\) are annual and daily GPP;  
\[GPP_{daily}=0.45\cdot\mathrm{e_{max}}.TMIN_{scalar}.VPD_{scalar}.SWR.FPAR \tag{2}\]  
where \(\mathrm{e_{max}}\) [kgC/MJ] is the maximum radiation conversion efficiency (extracted from BIOME-BGC, an ecosystem process model that estimates storage and flux of carbon, nitrogen, and water; [[https://www.ntsg.umt.edu/project/biome-bgc.php](https://www.ntsg.umt.edu/project/biome-bgc.php)]([https://www.ntsg.umt.edu/project/biome-bgc.php](https://www.ntsg.umt.edu/project/biome-bgc.php)) (accessed on 20 February 2025)) and the MODIS MOD12 product). \(TMIN_{scalar}\) is the scaled [0, 1] daily minimal ambient temperature; \(VPD_{scalar}\) is the scaled [0, 1] daily daylight average vapor pressure deficit, and SWR [W/m\({}^{2}\)/ha] is net incident shortwave radiation. \(TMIN_{scalar}\), \(VPD_{scalar}\), and SWR are extracted from the BPLUT. FPAR is the fraction of absorbed photosynthetically active radiation extracted from the MODIS MOD15 product.  
\[NPP_{annual}=0.8\cdot(GPP_{annual}-Rm) \tag{3}\]  
where \(NPP_{annual}\) is the annual net primary productivity, and \(Rm\) is plant maintenance respiration.  
The Enhanced Vegetation Index data were extracted from the MODIS MOD13Q1 product [28]. These data indicate the health status of the vegetation cover with a spatial resolution of 250 m. Time series of GPP, NPP, and EVI data were obtained from the EarthData geoportal ([[https://www.earthdata.nasa.gov](https://www.earthdata.nasa.gov)]([https://www.earthdata.nasa.gov](https://www.earthdata.nasa.gov)) (accessed on 20 February 2025)). The original NPP, GPP, and EVI data were converted into multi-band images for further analysis.  
Maps of GPP, NPP, and EVI trends were created using the Theil-Sen algorithm (\(p<0.05\)). The Theil-Sen estimator is a non-parametric robust method that fits a regression line through the median of slopes determined by all pairs of sample points [29]. \"Delta\" maps of NPP, GPP, and EVI changes (between 2001-2009 and 2010-2024) were calculated using the Mann-Whitney U-test.
## 3 Proposed Model and Problem Formulation
### Vehicle Communication Scenario
Thus, PDR can be expressed as [34]: \[SINR_{AVV1}=\frac{P_{AV}|h_{AVV1}|^{2}}{I_{V1}+N_{o}B} \tag{9}\]9 (39)
4. Packet loss rate (\(PLR\)): packet loss rate occurs when the destination fails to receive one or more pieces of transmitted information travelling across a network. It is typically caused by the physical channel of the wireless network, or due to network congestion. Then, the packet loss rate can be calculated mathematically as the percentage of packets lost with respect to packets sent. The percentage of packets lost is measured as the difference between the numbers of sent packets and received packets. Thus, it is defined by the following equation [35]: \[SINR_{AVX,V1X}=\frac{P_{AV}|h_{AVX}|^{2}}{I_{1X}+N_{o}B}+\frac{P_{V}|h_{V1X }|^{2}}{I_{2X}+N_{o}B} \tag{10}\]0 (40)
5. Average delivery latency (\(DL\)): the word latency is an important term that should be used to indicate any kind of delay in the network. Additionally, it can be defined as the total time taken by the transmitted data packet from one node to reach another node over a network; it is also called the network average-end-to-end-delay. Thus, the average delivery latency can be measured as the sum of the amount of time spent from the beginning of the transmission until the successful delivery of the packets at the destination over the total number of received packets by the destination [34]: \[SINR_{AVX,V1X}=\frac{P_{AV}|h_{AVX}|^{2}}{I_{1X}+N_{o}B}+\frac{P_{V}|h_{V1X }|^{2}}{I_{2X}+N_{o}B} \tag{10}\]1 (41)
# Dynamic Round Robin CPU Scheduling Algorithm Based on K-Means Clustering Technique
## 1 Introduction
### Clustering Technique
The pseudo-code of K-means Algorithm 1 is as follows:  
```
Input -Dataset -number of clusters Output -K clusters Step-1: -Initialize K centers of the cluster Step-2: -Repeat -Calculate the mean of all the objects belonging to that cluster \(\mu_{k}=\frac{1}{N_{k}}\ \sum_{q=1}^{N_{k}}x_{q}\)  where \(\mu_{k}\) is the mean of cluster \(k\) and \(N_{k}\) is the number of points belonging to that cluster -Assign objects to the closest cluster centroid -Update cluster centroids based on the assignment -Until centroids do not change
```  
**Algorithm 1** K-Means  
Determining optimal number of clusters in a dataset is an essential issue in clustering. Many cluster evaluation techniques have been proposed, one of which is the Silhouette method. Silhouette method measures the quality of a clustering; it determines how well each data point lies within its cluster. A high average silhouette width indicates a good clustering [29]. The Silhouette method can be summarized as follows:  
1. Compute clustering algorithm for different values of \(k\). For instance, by varying \(k\) from \(1\) to \(10\) clusters. 2. For each \(k\), calculate total Within-cluster Sum of Square (WSS). 3. Plot the curve of WSS according to the value of \(k\). 4. The location of a knee in the curve indicates the appropriate number of clusters. The Silhouette coefficient (\(S_{i}\)) of the _ith_ data point is defined in Equation (3). \[S_{i}=\frac{b_{i}a_{i}}{\max(b_{i},a_{i})} \tag{3}\]  
where \(b_{i}\), is the average distance between the _ith_ data point and all data points in different clusters; \(a_{i}\), is the average distance between the _ith_ data point and all other data points in the same cluster [30; 31]. **Motivation:** Timesharing systems depend on the time slice used in RR scheduling algorithm. Overheads of more context switches (resulted from choosing short time slice), and starvation (resulted from choosing long time slice) should be avoided. **Organization:** The rest of this paper is divided as follows: Section 2 discusses the related work. Section 3 presents the proposed algorithm. The experimental implementation is discussed in Section 4. Section 5 concludes this research work (see Figure 1).