esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
With the growing demand for industrial robots in the aerospace manufacturing process, the lack of positioning accuracy has become a critical factor limiting their broad application in precision manufacturing. To enhance robot positioning accuracy, one crucial approach is to analyze the distribution patterns of robot errors and leverage spatial similarity for error prediction and compensation. However, existing methods in Cartesian space struggle to achieve accurate error estimation when the robot is loaded or the end-effector orientations are varied. To address these challenges, a novel method for robot error prediction and accuracy compensation within configuration space is proposed. The analysis of robot error distribution reveals that the spatial similarity of robot errors is more pronounced and stable in configuration space compared to Cartesian space, and this property exhibits significant anisotropy across joint dimensions. A spatial-interpolation-based unbiased estimation method with joint weights optimization is proposed for robot errors prediction, and the particle filter method is utilized to search for the optimal joint weights, enhancing the anisotropic characteristics of the prediction model. Based on the robot error prediction model, a cyclic searching method is employed to directly compensate for the joint angles. An experimental system is established using an industrial robot equipped with a 120 kg end-effector and a laser tracker. Eighty sampling points with diverse poses are randomly selected within the task workspace to measure the robot errors before and after compensation. The proposed method achieves an error prediction accuracy of 0.172 mm, reducing the robot error from the original 4.96 mm to 0.28 mm, thus meeting the stringent accuracy requirements for hole machining in robotic aerospace assembly processes.  
robotic positioning compensation; robot error prediction; configuration space; spatial similarity; joint weights optimization; robot calibration +
Footnote †: journal: applied sciences  
Article  
A Robot Error Prediction and Compensation Method Using Joint Weights Optimization Within Configuration Space
## 3 Results  
### Fatty Acid Composition and Distribution between sn-2 and sn-1,3 Positions of Triacylglycerols  
As a result of extraction process, strawberry, black current and cranberry seed oils were obtained with the yield of 11%, 5% and 12%, respectively. The studied oils were tested using gas chromatography to analyze the composition of fatty acids. The results obtained for cranberry, black currant and strawberry seed oils, the percentage content of fatty acids from particular groups of saturated (SFA), monounsaturated (MUFA) and polyunsaturated fatty acids (PUFA) and the ratio of n-3 to n-6 fatty acids are presented in Table 1. Black currant seed oil was characterized by a very high content of linoleic acid (18:2 n-6c). It also contains large amounts of oleic acid (18:1 n-9c), alpha-linolenic acid (18:3 n-3) and gamma-linolenic acid (18:3 n-6). According to [PERSON] and [PERSON] [12], the high content of gamma-linolenic acid is characteristic of black currant seed oil. Strawberry seed oil was a source of large amounts of linoleic acid (18:2 n-6c), alpha-linolenic acid (18:3 n-3) and oleic acid (18:1 n-9c). These values are consistent with the literature, despite the use of different extraction methods. Among the oils tested in the study, strawberry seed oil was distinguished by a low content of saturated acids (SFA), especially palmitic acid (16:0), and a lack of gamma-linolenic acid. Cranberry seed oil was characterized by the highest content of oleic acid (18:1 n-9c) and alpha-linolenic acid (18:3 n-3) among the tested oils, and the lowest content of linoleic acid (18:2 n-6c). Close to trace amounts of gamma-linolenic acid (18:3 n-6) have been detected. Based on the obtained results, it can be stated that black currant, strawberry and cranberry seed oils are a rich source of polyunsaturated fatty acids. They are also characterized by a high content of monounsaturated fatty acids and low content of saturated fatty acids. Consuming products rich in polyunsaturated fatty acids is very beneficial for human health; additionally, it is crucial to maintain the right proportion of n-3 to n-6 fatty acids. The Western diet is characterized by an increased ratio of n-6 to n-3 fatty acids. Medicine associates this fact with an increased incidence of diseases, such as diabetes, rheumatoid arthritis, inflammatory bowel disease, obesity, asthma, cancer or depression, among the population, because when excessive amounts of n-6 fatty acids are consumed, pro-inflammatory eicosanoids are produced and, consequently, it can result in an inappropriate pro-inflammatory response of the immune system. On the other hand, eicosanoids, which are formed from n-3 fatty acids in the metabolic pathway, are thought to have anti-inflammatory and health benefits. It is proved that the optimal ratio of n-3 to n-6 fatty acids should be between 1:1 and 1:4 [23,24]. The oils tested in the study are characterized by an appropriate ratio of n-3 to n-6 acids. The ratio of these fatty acids for black currant seeds, strawberry seeds and cranberry seed oils are 1:4, 1:1.5 and 1:1.2, respectively.
## 2 Materials and Methods
### Preliminary Study to Evaluate the Amount of SSA to Incorporate in the Asphalt Mixtures
#### 2.4.1 Particle Size Adjustment of Asphalt Mixtures for Different SSA Incorporation Rates  
The asphalt mixtures to be studied were initially designed based on the various aggregate fractions' density and particle size distribution, adjusting the final grading curves to the specified grading envelopes [26]. Two surface course mixtures (AC 14 surf 35/50 BB) and two binder/base course mixtures (AC 20 bin/base 35/50 MB) were studied, taking into account the different SSA incorporation rates (50% or nearly 100%). Due to the low presence of fine material in the SSA, the maximum rates of SSA incorporation were 80% in the surface course mixture (AC 14) and 95% in the binder/base course mixture (AC 20).
## 2 II-36, IL-37, and IL-38: A Complex Group of Pro- and Anti-Inflammatory Cytokines
### Receptors and Intracellular Signaling
#### 2.2.1 IL-36 and IL-36 Ra  
IL-36\(\alpha\), \(\beta\), and \(\gamma\) bind their cognate receptor IL-1 Rrp2 and mediate the recruitment of the common chain IL-1 RAcP. The subsequent dimerization of the cytosolic TIR domains and the recruitment of post-receptor signal transducers lead to the formation of a complex signalosome. This activates mitogen-activated protein kinases (MAPK) and nuclear factor-kappa B (NF\(\kappa\)B) pathways [24; 25] via several mediators, such as the IL-1R-associated kinases (IRAK) (1 and 4) and the signaling adaptor TNF receptor-associated factor 6 (TRAF6).  
Overall, the activation of IL-36-dependent downstream signaling pathways induces the upregulation of proinflammatory genes including _IL-8_ or _IL-6_. The A471T polymorphism of the  
Figure 1: Overview of the IL-1, IL-36, IL-37, and IL-38 receptors and intracellular signaling. IL-1ß binds the IL-1R1 receptor. Activated IL-1R1 recruits the IL-1 RAcP common subunit and enables MyD88 to form a complex of signalization with IRAK and TRAF, which leads in turn to the phosphorylation/activation of the downstream signaling. A counter-regulatory pathway is represented by IL-1ß binding its decoy receptor IL-1R2 (membrane-bound or soluble) (not shown). IL-36\(\alpha\), \(\beta\), and \(\gamma\) bind to the IL-1 Rrp2 and recruit the common subunit IL-1 RAcP, inducing similar downstream signaling cascades as IL-1ß. IL-37 binds IL-18R\(\alpha\), which recruits IL-1R8 instead of the usual IL-18R\(\beta\), causing sequestration of Myd88 and transduction of a weak signal because of the IL-1R8 mutated intracellular domain. IL-38 might be able to bind IL-1R1, IL-1 Rrp2 and/or IL-1 RAPL1 but further studies need to confirm its preferential intracellular mechanism of action. IL1R = IL-1 Receptor; IL-1 RAcp = IL-1 Receptor Accessory Protein; IL-1 RAPL1 = IL-1 Receptor Accessory Protein Like 1; IL-1 Rrp2 (or IL-1 RLR2) = IL-1 Receptor Like 2; MyD88 = Myeloid Differentiation Primary Response Protein 88; IRAK = IL-1R-Associated Kinase; TRAF = TNF Receptor-Associated Factor; JNK = Jun N-terminal Kinase; AP1 = Activator Protein 1; NF\(\kappa\)B = Nuclear Factor-kappa B; MAPK = Mitogen-Activated Protein kinases.  
IL1 Rrp2 TIR domain, occurring in 2% of the population, leads to a reduced IL-36R signalization by diminishing the interaction between the two elements of the receptor (IL-1 Rrp2 and IL-1 RAcP) [26]. IL-36 Ra specifically antagonizes the proinflammatory activity of the three IL-36 cytokines by binding IL-1 Rrp2 with a higher affinity than the agonists [27]. IL-36 Ra subsequently prevents the recruitment of the common subunit IL-1 RAcP, mirroring the same inhibitory mechanism used by IL-1 Ra for antagonizing IL-1\(\alpha\) and \(\beta\).
## 3 Results and Discussion  
### Mass Loss and Water Content  
The water content in bread is crucial for determining its quality and texture [28]. Generally, bread water content (ratio of bread water mass over total mass) is around 30% to 40% [29; 36]. However, flatbreads generally have a lower water content than French-style breads due to their larger surface area-to-volume ratio [30].  
Figure 4: Millimetric XY chart is used to record dough expansion during baking.  
In this study, water content in Lebanese flathead was primarily influenced by baking temperature and water evaporation. Table 3 shows the water content of dough and bread after baking for two trials. For each test, 100 g of dough was used, and 20 pieces were baked under identical conditions over two days. The results indicate that the water loss due to evaporation corresponds to 14.8% of the initial dough mass, with a maximum loss of 16 g and a minimum of 12 g.  
The water content values presented in Table 3 are calculated on a wet basis, where they are expressed as a percentage of the total mass of the dough or bread, including both water and dry ingredients.  
Moisture content was calculated based on the difference between the amount of water added during dough preparation and the water lost during baking. While this method introduces potential measurement errors, the results reported are the average of triplicate measurements.  
These results show that the water content in baked Lebanese bread is remarkably lower than in Western bread (30% to 50%) [37].  
The mass loss in Lebanese bread baking is greater than in other flatbreads, such as chapati, which shows an average weight loss of 9% [38]. This difference is likely due to the significantly higher baking temperature used for Lebanese bread (~650 \({}^{\circ}\)C) [2], compared to the chapati baking temperature (~250 \({}^{\circ}\)C) [38], despite similar baking durations. Due to its thin structure and exposure to such high heat, Lebanese bread experiences substantial water loss, typically resulting in a final moisture content of around 20%. In contrast, breads like naan and Western-style leaves retain more moisture, with final water contents ranging from 30% to 50% [32; 39]. The short baking time at extreme temperatures also distinguishes Lebanese bread from breads such as French loaves, which are baked for approximately 22 min at 250 \({}^{\circ}\)C [32]. These baking conditions also contribute to the rapid crust formation and distinctive pocket formation. The high temperature in Lebanese bread causes the crust to form quickly, which acts as a barrier to further water loss [40] and preserves the softness of the interior [41]. In contrast, other flatbreads such as naan, bake at lower temperatures, which results in a more uniform texture and slower evaporation of water during baking.
# A Review of Electrochemical Sensors for the Detection of Glyated Hemoglobin
## 1 Introduction
Specifically, three major enzyme types will be discussed: (1) FAO type, (2) FPOX, type and (3) MIC type. The overall classification of electrochemical HbA1c sensors discussed in this paper is shown in Figure 1.  
According to the indicators of general household testing equipment, the performance and potential applications of HbA1c biosensors in diabetes diagnosis can be evaluated from five perspectives: (1) suitability of their detection range for clinical use, (2) detection limit, (3) detection time, (4) sensitivity, and (5) continuous stability [27; 28]. In this paper, we intend to provide a comprehensive review of the advances in electrochemical sensors for HbA1c detection in recent years and compare the advantages and disadvantages of these sensors from the above five perspectives. Finally, we elaborate on the challenges to address in the development of commercially successful HbA1c sensors, such as sensitivity, stability, continuity, and in situ monitoring in a complex environment.
# Effect of AQM-Based RLC Buffer Management on the eNB Scheduling Algorithm in LTE Network +
## 4 Active Queue Management and Proposed Approach
Depending on the value of the parameter \(i\), we define SmRED's different versions as SmRED-\(i\). In summary, the expressions of SmRED-\(i\)'s packet dropping probability, being the function of the average queue length, are shown in Equation (5). \[P_{d}= \begin{cases}0,avg\in[0,Min_{th})\\ P_{max}\times\left(\frac{avg-Min_{th}}{Max_{th}-Min_{th}}\right)^{i},avg\in[ Min_{th},Target)\\ P_{max}\times\left(\frac{avg-Min_{th}}{Max_{th}-Min_{th}}\right)^{1/i},avg\in[ Target,Max_{th})\\ 1,avg\in[Max_{th},+\infty)\end{cases}  \tag{5}\]  
The detailed proposed discard strategy of the algorithm can be described as shown in Figure 5. Essentially, one cannot simultaneously have a high link utilization and low queuing delays. Therefore, a reasonable trade-off is required between these two performance measures [41]. Tuning the value of the parameter \(i\) gives a trade-off between high throughput and low delay among different versions of SmRED. The effectiveness of our proposed scheme is presented via simulation in Section 6. Figure 4: The packet dropping probability function.
# Research on Advancing Radio Wave Source Localization Technology Through UAV Path Optimization
## 3 Materials and Methods
### Objective Function  
One of the Rx candidate points is designated as the receiver (rx). For all 144 transmitters, the estimated coordinates are computed through pattern matching with the location fingerprint database. The UAV trajectory is determined to minimize the 90 th percentile value of the cumulative distribution function (CDF) across all 144 transmitters.
# An Effective Hybrid Routing Algorithm in WSN:
## 4 ACOHCM Routing Algorithm
### Pheromone Update Strategy  
The pheromone update strategy in the ACOMHC algorithm is as follows: In the end of each iteration, only the pheromone values of the optimal path in this iteration are updated, and the pheromone values of the other paths are partially volatilized; In order to avoid the unlimited growth or reduction of the pheromone on the certain paths, ACOHCM limits the pheromone values by setting the upper pheromone limit \(\tau_{max}\) and the lower pheromone limit \(\tau_{min}\). If the updated pheromone exceeds the upper or lower limit, the pheromone is assigned upper pheromone limit or lower pheromone limit. Therefore, the ants have probability to try taking other paths, which increases the ability of the algorithm to find the global optimal solution and prevents the algorithm from prematurely converging to the local optimal solution. Using this pheromone update strategy, obviously can speed up the convergence of the algorithm, as well as overcome the stagnation of ACO algorithm.  
The improved pheromone update formula is defined as in Formula (7).  
\[\tau_{ij}(t+n)=\begin{cases}(1-\rho)\tau_{ij}(t),&L_{ij}\text{ is not on the optimal path \& }(1-\rho)\tau_{ij}(t){>}\tau_{min}\\ (1-\rho)\tau_{ij}(t)+\Delta\tau_{ij},&L_{ij}\text{ is on the optimal path \& }\tau_{min}<(1-\rho)\tau_{ij}(t)<\tau_{max}\\ \tau_{max},&L_{ij}\text{ is on the optimal path \& }(1-\rho)\tau_{ij}(t)+\Delta\tau_{ij}\geq\tau_{max}\\ \tau_{min},&L_{ij}\text{ is on the optimal path \& }(1-\rho)\tau_{ij}(t)+\Delta\tau_{ij}\leq\tau_{min},\text{or}\\ &L_{ij}\text{ is not on the optimal path \& }(1-\rho)\tau_{ij}(t)\leq\tau_{min}\\ \end{cases} \tag{7}\]  
\(\Delta\tau_{ij}=\sum_{k=1}^{m}\Delta\tau_{ij}^{k}\)\(m\) is the number of the ants:  
\[\Delta\tau_{ij}^{k}=\begin{cases}\frac{Q}{L_{k}},&\text{ant $k$ goes pass the path $L_{ij}$}\\ 0,&\text{others}\end{cases} \tag{8}\]  
\(\rho(0<\rho<1)\) is a pheromone evaporation factor, and \((1-\rho)\) is pheromone residual factor; \(\Delta\tau_{ij}\) is the pheromone that the ants deposit on the path \(L_{ij}\) in the current iteration; \(\Delta\tau_{ij}^{k}\) is the pheromone that the ant \(k\) deposits on the path \(L_{ij}\) in the iteration; \(Q\) is a constant and it affects the convergence speed of algorithm to a certain extent; \(L_{k}\) is the length of path that the ant \(k\) traverses in the iteration. It is thus clear that the smaller the \(L_{k}\) is, the more pheromone that the ant deposits on the path, which is helpful in searching for the optimal path.
## 6 Experimental Results and Discussion
### Accuracy Experiment  
The experimental accuracy of our algorithm was evaluated using the warehouse environment. As a reference, the result of the SICK NAV350 2D laser navigation system was taken as the ground truth trajectory, as shown in Figure 10. In addition, a state-of-the-art method was compared with our algorithm. Accuracy was compared by computing the absolute translation root mean squared error (RMSE) between the trajectory of the SLAM result and the ground truth. Each algorithm was run five times on multiple sequences, and the average RMSE was taken as the final result. We plotted the trajectories of the localization results of our algorithm and ORB-SLAM2, as well as the trajectories of  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Ours**} & \multicolumn{5}{c}{**ORB-SLAM2**} \\ \cline{2-9}  & **Min (ms)** & **Mean (ms)** & **Max (ms)** & **STD** & **Min (ms)** & **Mean (ms)** & **Max (ms)** & **STD** \\ \hline Tracking & **6.53** & **17.15** & **99.10** & **17.69** & 75.98 & 112.39 & 287.88 & 23.06 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Time statistics for the detection of 2000 keypoints in the dataset.  
Figure 9: Comparison of tracking performances between our algorithm and ORB-SLAM2. The peak in our tracking time corresponds to the addition of keyframes, which need more time to extract features and match them, update the depth of keypoints, and optimize camera pose.  
the ground truth obtained by the laser navigation system. It can be seen that both methods achieved high localization accuracy.  
Table 3 shows the accuracy performance on the warehouse dataset of our method and ORB-SLAM2. The RMSE results for our system are provided in Table 3. In addition, our method was compared with the state-of-the-art approach ORB-SLAM2. The table lists RMSE values of the absolute trajectory error for the two methods, and our method achieved comparable accuracy to ORB-SLAM2 and even exceeded its accuracy for some sequences. It should be noted that this high accuracy was achieved by our method while greatly reducing the tracking time because of the hybrid tracking model and the kinematic model of the forklift in Section 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{2}{c}{**Ours**} & \multicolumn{2}{c}{**ORB-SLAM2**} \\ \cline{2-5}  & **RMSE** & **STD** & **RMSE** & **STD** \\ \hline
1 & **0.16** & 0.01 & 0.19 & 0.01 \\
2 & 0.11 & 0.01 & **0.10** & 0.008 \\
3 & **0.15** & 0.006 & 0.33 & 0.017 \\
4 & **0.23** & 0.035 & 0.35 & 0.01 \\
5 & 0.14 & 0.01 & **0.09** & 0.005 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy performance on the warehouse dataset.  
Figure 10: Accuracy performance of our method and ORB-SLAM2 for different sequences using a laser sensor as the ground truth. (**a**) Ours-a. (**b**) ORB-SLAM2-a. (**c**) Ours-b. (**d**) ORB-SLAM2-b.
###### Abstract  
Healthcare data are considered sensitive and confidential, and storing these sensitive data in traditional (i.e., centralized) databases may expose risks, such as penetration or data leaks. Furthermore, patients may have incomplete health records since they visit various healthcare centers and leave their data scattered in different places. One solution to resolve these problems and permit patients to own their records is a decentralized personal health record (PHR); this can be achieved through decentralization and distribution systems, which are fundamental attributes of blockchain technology. Additionally, the requirements for this solution should be identified to provide practical solutions for stakeholders. This study aims to identify the key requirements for PHRs. A design science methodology was utilized to meet the study objectives, and thirteen healthcare experts were interviewed to elicit the requirements and the previous studies. Thirty-three requirements are defined, and based on these, high- and low-level architectures are developed and explained. The result illustrates that the developed solution-based Hyperledger Fabric framework is a promising method for the achievement of PHRs that guarantee security aspects, such as integrity, confidentiality, privacy, traceability, and access control.  
accs control; blockchain technology; data owner; identity management; personal health records +
Footnote †: journal:  
source across numerous healthcare organizations after obtaining the patient's approval [3], thus reducing the cost paid by patients for a service (i.e., affordability) and saving time [4]. On the other hand, the disclosure of patient data through unauthorized access or data breaches remains a challenge as it is a threat to patient privacy. Moreover, according to [PERSON] [5], many legitimate healthcare providers earn profit from selling these data. Thus, patients must be aware of who has permission to view their data and the purposes for which it is used; on top of this, patients should be the ones who benefit first from these data. Several laws and regulations have been enacted, such as the Health Insurance Portability and Accountability Act (HIPAA) [6] and the General Data Protection Regulation (GDPR) [7], to enable the application of guidelines and compliance in the healthcare industry on how to manage, process, and safeguard personal data; the recent attacks on the healthcare industry demonstrate that the sector's data security has challenges, as hackers find it an easy target [8].
# Insights into the Issue of Deploying a Private LoRaWAN
## 4 Experimental and Analytical Results
### Results Discussion  
The private approach has a slight advantage over the public approach, because of the possibility of customizing the frequency plan and many other variables. Moreover, our results show a clear advantage of the private network's performance for an end-user, whose devices are located in a reasonably small geographical area, with respect to coverage (indoor), signal strength, signal propagation, or loss rate. However, the private network will need to increase the number of gateways to provide sufficient communication performance for an increased number of devices or for mobile devices. We provided a methodology for estimating the expenses which impact both of the LoRaWAN approaches. However, these need to be brought into a real-case context. Meanwhile, security-wise, the private approach offers, again, a slight advantage and a higher level of security, mostly thanks to the private key management without a third party and the possibility of improving the internal security mechanisms.