esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Session-Aware Recommenders: A Deep Dive into GRU-Based Latent Interaction Integration
## 4 Experimental Results
### Performance Comparisons  
#### 4.4.1 Performance of Different Feature Fusion Strategies  
Given that the Amazon dataset is compatible with all recommendation methods under consideration, we initially selected the feature fusion method based on this dataset. The optimal solution identified here was then applied to subsequent experimental analyses. For these experiments, we utilized the LCII model. Three feature fusion strategies were examined: sum, element-wise multiplication (\(\odot\)), and the attention-gate mechanism. The experimental results are presented in Table 3, where the best result is highlighted in bold and the second-best score is underlined, as will be marked in subsequent charts.  
The attention-gate mechanism mitigates conflicts in fusion between data with differing feature content. By leveraging calculated weights, attention-gate assesses the influence of each feature on the model, enabling it to prioritize certain feature information over others during different time periods. This approach effectively reduces the dominance of one feature type over another. In terms of prediction accuracy, without considering sorting, attention-gate performs the best. When sorting, indices  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Feature** & **Recall** & **MRR** & **NDCG** & **Recall** & **MRR** & **NDCG** & **Recall** & **MRR** & **NDCG** \\
**Fusion** & **@5** & **@5** & **@5** & **@10** & **@10** & **@10** & **@20** & **@20** & **@20** \\ \hline Sum & 0.2313 & **0.2208** & 0.1973 & 0.2339 & **0.2212** & 0.2132 & 0.2369 & **0.2214** & 0.2164 \\ Element-wise Multiplication & 0.2393 & 0.2194 & 0.2043 & 0.2475 & 0.2205 & 0.2224 & 0.2545 & 0.2210 & 0.2270 \\ Attention-Gate & **0.2407** & 0.2157 & **0.2060** & **0.2487** & 0.2168 & **0.2244** & **0.2553** & 0.2173 & **0.2300** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance w.r.t. feature fusion strategies.  
attention-gate still competes closely with the other two fusion methods. Therefore, based on these experimental results, the attention-gate mechanism will be used for experimental analysis in subsequent comparison models.
## 2 Materials and Methods
### Optimal Number of Anchors  
Prior knowledge plays a vital role in guiding machine learning. An anchor is a kind of prior knowledge provided in advance, which can point out the possible size of the target box to speed up the learning ability of the model. Since YOLOv5 is an anchor-based model, the setting of the initial anchor significantly affects the convergence speed and effectiveness of the model.  
According to the input bounding box size, YOLOv5 uses the K-means algorithm to pre-calculate K optimal anchors for each feature layer so that the deviation from the object pre-position is small. However, due to the view field and super-high resolution, the target scale in the image changes drastically. Therefore, YOLOv5's preset three-scale anchors for each feature layer are unsuitable for gigapixel-level images. Hence, each feature layer needs to preset more anchors to adapt to the scale change of the target.
## 2 Sliding Mode Control Design
### Second Step: Sliding Surface Proposal  
The sliding surface \((S)\) proposed in this paper is composed of the terms \(S_{1},S_{2}\) and \(S_{3}\) as indicated in (3). Their respective sliding coefficients are \(\alpha_{1}\), \(\alpha_{2}\) and \(\alpha_{3}\). \(S_{1}\) corresponds to the normalized DC voltage error where \(V_{ref}\) is the DC voltage established as a requirement in the design process [29]. \(S_{2}\) corresponds to the AC current error, where the reference current (\(i_{ref}\)) is a rectified sinusoidal signal being \(I_{ref}\) its amplitude, as indicated by (4). \(I_{ref}\) is obtained through the power balance between SBBC input and output \((P=v_{s}i_{ref}=V_{ref}i_{o})\). \(S_{3}\) corresponds to the integral of the AC current error used to increase system stability. This integral reduces the amplitude of oscillations in start-up for input current or in face of large disturbances.  
\[S=-\alpha_{1}\underbrace{\left(\frac{v_{o}}{V_{ref}}-1\right)}_{S_{1}}-\alpha_{2} \underbrace{\left(i_{s}-i_{ref}\right)}_{S_{2}}-\alpha_{3}\underbrace{\left(i_ {s}-i_{ref}\right)dt}_{S_{3}} \tag{3}\]  
\[i_{ref}=\underbrace{\left(\frac{2V_{ref}v_{o}}{RV_{s}}\right)}_{l_{ref}}|sin( wt)| \tag{4}\]  
Equations (1)-(3) are conveniently expressed using \(x_{1}=(v_{o}/V_{ref}-1)\) and \(x_{2}=i_{s}-i_{ref}\) as follows:  
\[\frac{dx_{1}}{dt}=\frac{(1-u)(x_{2}+i_{ref})}{CV_{ref}}-\frac{(x_{1}+1)}{RC} \tag{5}\]  
\[\frac{dx_{2}}{dt}=\frac{v_{s}}{L}-\frac{V_{ref}(1-u)(x_{1}+1)}{L}-\frac{di_{ ref}}{dt} \tag{6}\]  
\[S=-\alpha_{1}x_{1}-\alpha_{2}x_{2}-\alpha_{3}\int x_{2}dt \tag{7}\]  
Equation (8) represents the reference current time derivative \((di_{ref}/dt)\) where coefficients \(\gamma_{1}\) and \(\gamma_{2}\) are defined as auxiliary variables. Equations (6) and (8) can be combined for deducing the dynamics of current error in (9).  
\[\frac{di_{ref}}{dt}=\underbrace{\left(\frac{2V_{ref}^{2}}{RV_{s}}|sin(wt)| \right)}_{\gamma_{1}}\frac{dx_{1}}{dt}-\underbrace{\left(\frac{2 wV_{ref}^{2}} {RV_{s}}cos(wt)sign[sin(wt)]\right)}_{\gamma_{2}}(x_{1}+1) \tag{8}\]  
\[\frac{dx_{2}}{dt}=\frac{v_{s}}{L}+\left[\frac{\gamma_{1}}{RC}-\gamma_{2}-\frac {(1-u)V_{ref}}{L}\right](x_{1}+1)-\frac{\gamma_{1}(1-u)}{CV_{ref}}(x_{2}+i_{ ref}) \tag{9}\]
# Feasibility of Using Low-Cost Dual-Frequency GNSS Receivers for Land Surveying
## 3 Results and Discussion
### Static Positioning
#### 3.2.2 Positioning Confidence and Accuracy  
Estimated uncertainty of coordinates is another indicator of the quality of a positioning solution. For 1-h-long sessions and relative positioning, the uncertainty at a 95% confidence level was similar for all points and, surprisingly, did not depend on baseline length (Figure 6a). For S1 points measured in the open-sky conditions, as well as for points measured in the urban area (S1, S2, S3, S4, S5) similar \(2\sigma\) uncertainties of less than 1 mm were achieved for horizontal coordinates and between 2 to 3 mm for the vertical component. With PPP (Figure 6b), the uncertainties resulted much higher and depended on the satellite visibility, i.e., they were significantly lower for S1 than for the other points. The decimeter level \(2\sigma\) uncertainties achieved for the urban environment during 1-h-long sessions did not meet the requirements of precise positioning. Even in the open sky conditions, poor uncertainties (\(2\sigma\)) were achieved, i.e., they were lower than 20 mm and higher than 50 mm for the horizontal and vertical components, respectively.  
The differences of coordinates with respect to reference data (Figure 7) resulted significantly higher in the relative positioning and much lower in the absolute positioning rather than corresponding uncertainties. Nevertheless, in the relative positioning, the centimeter-level accuracy was achieved both for the horizontal and vertical components. The corresponding root means square errors (RMSE) are 11, 17, and 15 mm for the North, East, and Up component, respectively. The worst accuracy was achieved for the S1 coordinates determined by using the 50-km-long baseline (WIEL). With PPP, coordinate differences resulted lower than 1 decimeter. Only for S1 was c  
Figure 5: Coordinate differences of point S1 obtained for various session lengths: (**a**) in the relative positioning using the reference station KALI; (**b**) in the relative positioning using reference station WIEL; (**c**) in the absolute positioning.  
achieved, due to the open sky measurement conditions. The RMSE were 20, 58, and 56 mm, for the North, East, and Up components, respectively.
## 3 Proposed Methods
### Preliminaries and Assumptions  
In the beginning, sensors are randomly deployed within the region of interest (RoI). These deployed sensors are homogeneous, with the same communication range and sensing range. Each sensor is moveable and can accurately obtain its position. All sensors periodically send their sensed data, identity, and position information to the sink node if the newly sampled data are different from the last ones. The continuous object has an irregular shape. We define that a sensor detects the event if it is within the coverage of the continuous object and its sensed concentration is higher than the threshold.
# A Supervised Neural Network Control for Magnetorheological Damper in an Aircraft Landing Gear
## 4 Hybrid Controller with Load Cell Data and Known Aircraft Mass
### Experimental Results  
In order to confirm the performance of the hybrid controller, drop test experiments were executed with various aircraft mass and sink speeds. Figure 8 shows the comparison results of the passive damper, skyhook controller, and hybrid controller from experiments in the case of \(m_{1}=190\) kg and \(v=1\) m/s. The hybrid controller attempted to maintain the maximum force after the first peak for as long as possible during the first compression, bringing the curve closer to a square than other ones. However, the hybrid controller could not completely compensate for the force error, mainly due to the response time of the MR core and the limit of the applied voltage for system safety.  
However, the hybrid controller still exhibited the highest shock absorber efficiency compared with the passive damper and skyhook controller. Table 1 presents the comparison results of the passive damper, skyhook controller, and hybrid controller for differing aircraft masses and sink speeds. It can be seen that the hybrid controller produced better landing gear performance than others. The hybrid controller exhibited the highest shock absorber efficiency in all test conditions and showed the smallest stroke and the smallest maximum ground reaction force in most of the conditions.  
Figure 7: Skyhook gain surface.
## 4 Results and Discussions
### Simulation Results
The comparison of yaw rate and front tire slip angle under PID and MPC tracking control are shown in Figure 9. Compared with MPC controller, PID controller with direct error feedback only carries out closed-loop control for a single target, which leads to a larger maximum and average front wheel slip angle. The maximum, average and standard deviation of the front wheel slip angle under PID control are 2.0653, 0.2494 and 0.5210, respectively. The maximum, average and standard deviation of the front wheel slip angle under MPC control are 0.9895, 0.1480 and 0.2921, respectively. The sideslip under PID control will expand as the curvature of the path to be tracked increases, eventually increasing the vehicle's risk of sideslip and instability. The single control step in the controller is distributed in 3-5 ms, which meets the demand of the control response.
## 5 Conclusions  
This paper proposes a spatial-temporal co-clustering algorithm based on information divergence, and its advantages are as follows.  
Unlike traditional clustering algorithms that only cluster rows or columns, the algorithm proposed by us performs clustering on both the rows and columns of a matrix. This makes it not only suitable for the field of spatial-temporal pattern discovery, but also other fields such as text mining and genetic engineering. It is an iterative optimization strategy that can be initialized randomly or in other effective ways, and then performs row-clustering and column-clustering in a loop. Because these clustering processes can be performed separately and in parallel, this algorithm is very efficient. In addition, the algorithm uses information divergence as a similarity measure, which has advantages when dealing with non-negative data, such as count data or probability distributions. This enables the algorithm to have better performance when processing such data.  
This paper demonstrates that the spatial-temporal co-clustering algorithm holds tremendous promise for discovering spatial-temporal patterns of in the Ocean Field. Under the assumption of mutual constraints in time and space, it can unearth potential patterns of spatial-temporal data. Our next moves will be in the two directions listed below:  
1. Our spatial-temporal co-clustering algorithm has only been applied in the South China Sea. This is constrained by our limited computing resources. We are exploring parallel distribution computing technique such as _Dask_. In the future, we will offer an algorithm implementation utilizing _Dask_, ideal for distributed systems like compute clusters. The parallel spatial-temporal co-clustering algorithm implemented based on _Dask_ will be applied globally to discover the spatiotemporal patterns of global SST.
2. The marine environment comprises diverse elements that interact with and impact each other. In future studies, we aim to extend our analysis beyond the spatial and temporal constraints by incorporating critical oceanic elements like salinity and sea surface height into collaborative clustering. This will help us investigate the potential of multi-element co-clustering in marine research.  
Conceptualization, Q.H. and Z.X.; methodology, Q.H. and Z.X.; software, Z.X.; validation, Z.X. and Q.H.; resources, L.G., Z.X. and D.H.; data curation, Z.X.; writing--original draft preparation, Z.X. and Y.D.; writing--review and editing, W.S., Q.H. and D.H.; funding acquisition, Q.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (No. 2021 YFC3101602), National Natural Science Foundation of China (No. 42376194).  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
**Acknowledgments:** We would like to thank the anonymous reviewers for their insightful com-ments and substantial help in improving this paper.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Weighted-CAPIC Caching Algorithm for Priority Traffic in Named Data Network
## 4 Weighted Cache Based on Popularity and Class (Weighted-CAPIC) Algorithm
As in Figure 1, when the interest message enters, the router will check its content store. If the content requested exists in the content store, it will be sent to the consumer by the router, and the copy will be kept in the one-hop downstream router. For every request, content gets closer to the consumer. The popularity of content determines on which nodes it will be stored in the network. The next step is router checks how much cache portion for keep the content in the router. This portion is determined based on the content class, using the Weighted-CAPIC formula. The Weighted-CAPIC algorithm calculates the total content requested over a specific frequency limit factor for each content class. The value of this parameter is described as \(X_{d}\). After that, the router will sort the values and assign a weight, \(\omega_{d}\), corresponding to them. The router further calculates the cache portion for the content class according to Equation (9). If the cache capacity for a class is full, the algorithm will run LRU as a cache replacement method. If the cache capacity for a class is full, the algorithm will run LRU as a cache replacement method. The router performs this two-step mechanism every time there is an incoming packet of interest and data (content). \[c_{d}=\frac{X_{d}.\omega_{d}}{\sum_{y=1}^{D}X_{y}.\sum_{m=1}^{D}\omega_{m}}C \tag{9}\]  
where:  
\(X_{d}=\) the number of variations in content class k that is requested more than a certain Frequency value;  
\(c_{d}=\) cache proportion for class \(d\);  
\(C=\) total content store capacity;  
\(D=\) number of content classes.
# Energy-Balanced Routing Algorithm Based on Ant Colony Optimization for Mobile Ad Hoc Networks
## 4 Simulations
### Performance of Routing Protocol
The reason is that the increase in load means an increase in energy consumption, but, because the nodes are constantly moving, the intermediate nodes are constantly changing, so the energy consumption is relatively balanced, and the death time of the first node does not change much. In addition, since IEMACO considers the energy consumption rate of the node and the remaining life time of the link in the path selection, it can actively avoid selecting nodes with fast energy consumption rate and fast node movement speed, and the efficiency of network load balance is higher than that of AntHocMMP. Figure 11 indicates a simulation performance of the number of dead nodes as the node's packet rate changes. As the load increases, the number of dead nodes increases continuously because the  
Figure 10: First node death time versus node packet rate. Figure 9: Packet delivery rate versus node packet rate. energy consumption accumulates continuously regardless of the primary path or multipath. As the simulation time increases, the node death is inevitable. When transmitting data, AOMDV first selects the primary path for transmission. Only when the primary path is unavailable is the backup path enabled. Therefore, the network load is mainly concentrated on one path, causing the node failure to occur frequently. AntHocMMP takes into account the residual energy of the node and the average energy of the link when routing, thus nodes with more energy will transmit more packets. In short, as the network load increases, the energy consumption of the node increases resulting in link failure. The data retransmission will bring longer end-to-end delay and packet loss rate. IEMACO considers the energy consumption rate of the node. In path selection, the nodes with less residual energy and heavy load will not be used, which can balance the network energy and prolong the service time of the network.
## 1 Introduction  
Diabetic symmetrical peripheral neuropathy (DSPN) is a well-known complication of diabetes mellitus types 1 and 2 [1, 2, 3, 4]. Symptoms may be diverse, depending on the affected nerve fibers [5]. The first symptoms usually include impaired thermal sensation, pain, or unpleasant sensation in the feet, especially at night. As the impairment of nerve fibers progresses, patients typically complain of numbness, tingling, pain, weakness, and hyposepreshesia; the latter two begin in the feet and spread in a stocking distribution. Symptoms are typically symmetric; however, sensory symptoms are more pronounced than motor symptoms [2, 3, 4, 6, 7]. The major underlying cause of DSPN is hyperglycemia, although dyslipidemia also plays an important role in diabetes mellitus type 2 [7]. Nerve blood flow impaired due to hyperglycemia increases oxidative stress and inflammation, usually first affecting small-diameter sensory nerves responsible for temperature sensation and painless injury [5, 6, 8]. The impairment of large fiber sensory nerves responsible for kinesthetic senses of joints and vibration is exhibited in decreased ankle reflexes and a loss of balance while walking [3; 4; 7]. Patients reporting a loss of sensation are at a higher risk of developing foot ulcers [5].  
Maintaining normoglycemia seems to be the only preventive measure to avoid or postpone DSPN. According to clinical trials, enhanced glucose control proved much more effective in type 1 diabetes than type 2 [7]. Despite careful treatment, the probability of developing DSPN over the years is approximately 30% and 50% for diabetes type 1 and type 2, respectively [8].  
Current treatment protocols include pain relief with opioids, antidepressants, anti-convulsants and many other medications still being evaluated and investigated [7; 8; 9; 10]. Because of limited effectiveness and known adverse effects, non-pharmacological treatment approaches like transcutaneous electrical nerve stimulation and monochromatic infrared energy therapy have been investigated [10; 11; 12; 13; 14; 15].  
Recently, our group investigated the effect of transcutaneous application of gaseous CO2 on diabetic chronic wound healing [16]. After only a few treatment sessions, patients reported finally sleeping through the night because the burning and tingling sensations in their feet had diminished or had even disappeared, which led us to perform further investigations. As described in our previous article, transcutaneous application of CO2 seems to have an immediate effect on vasodilation and elevates the oxygen release from Hb via the Bohr effect [16; 17; 18; 19]. It seems that, as the therapy is repeated, neoangiogenesis is induced [20; 21]. Since microvascular dysfunction plays an important role in DSPN, we expected transcutaneous CO2 application to improve sensation in patients with DSPN [6; 22]. Therefore, this study aimed to evaluate the effect of transcutaneous application of gaseous CO2 on diabetic symmetrical peripheral neuropathy.
# Simplified Polydispersion Analysis of Small-Angle Scattering Data
###### Abstract  
With polydisperse inhomogeneities, the analysis of small-angle scattering (SAS) data is possible by fitting the experimental data to theoretical models. Despite scientific software being available for this task, many scientists in different fields prefer other techniques for their investigations. With the simplified polydispersion analysis (SFA) presented here, it is possible to analyse the SAS data in a much simpler way. A straightforward interpolation of SAS data using any commercial software, requiring no advanced computational skills, allows the determination of the size distribution function (SDF) of the polydisperse inhomogeneities. Here, this innovative approach was tested against simulated SAS data of spherical inhomogeneities, as well as experimental data with excellent results. The results reported here offer new opportunities for many scientists to use the SAS technique to investigate polydisperse systems.  
small-angle scattering; polydispersion analysis +
Footnote †: journal: Applied Sciences