esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Findings and Results  
The project's evaluation presented in this paper was performed in the attempt of providing insights into the general parameters presented at the beginning of Section 4. Furthermore, the questionnaire was helpful in investigating the relationship between a non-anthropomorphic robot and its own level of likeability and acceptance. From the findings reported in Table 2, it is possible to observe how the grade of acceptance is generally quite high despite the perceived appearance of a machinelike agent (Av = 2.32 with a SD = 0.75) with artificial traits (Av = 3.31 with a SD = 0.97). Alongside the [PERSON] questionnaire, it was vital to pair the survey with on-site observations that have shed light on why, even if the movement of the robot is not perceived as \"very elegant\", the likeability of the robot is still high. According to the participants, _\"it was more important to have a robot who could move while performing the specific given task, rather than one that moves more fluidly but doesn't perform the instructed task\"_. Moreover, this perception of likeability is reinforced by the appeal of the user interface since, _\"Using an easy to use graphical interface makes me feel at ease with the robot, and I perceive it more like a companion, such as the Roomba\"_. The ability of the robot courier to perform specific tasks has been perceived positively, and has gained a high grade of competence (Av 4.00). In conclusion, the results suggest that, in order to reach a positive experience, the robot companion has to express its intentions and skills as clearly as possible, in order to avoid unmet expectations.  
Figure 8: Demographics of the participants. Out a total number of 48, 66.7% was male and 33% was female. The prominent age span was between 31 and 45 y/o.
# Indoor Positioning by Double Deep Q-Network in VLC-Based Empty Office Environment
## 4 Proposed Indoor Positioning Method
### Block Diagram of Proposed Method  
This section describes the block diagram of the proposed method in detail. The block diagram is shown in Figure 6.  
In Figure 6, the proposed method is divided into three steps. The first step is the fingerprinting. The core technology of the fingerprinting step is to determine the location of the RP and construct a fingerprinting database. The second step is to determine the initial state of the agent to reduce the processing time for positioning. For this purpose, the X and Y coordinates of the AP providing the strongest RSS were determined as the agent's initial state. The third step is the double deep Q-Network algorithm, which is the core of this study. The agent performs the 5 actions defined in the above section and receives a reward accordingly. Based on the reward provided, it learns the policy to select the optimal action. The double deep Q-Network model that has been taught outputs the final location of the UE.
## 6 Discussion
### Usability, Limitations and Future Development of the BPriS Method
* Diversity of software development projects--the BPriS method has been presented with utilization in development of new software product, but these are not the only types of software projects, such as enhancement of functional and non-functional aspects, i.e., different types of software maintenance projects, etc. However, the case study presented in this manuscript also presents the possible utilization of the method with project that focus on functional enhancements. * Complexity of data structures and related software development subtasks granularity--currently, the BPriS method did not address data structures from business process models, but only business processes in software functions design. Having complex data structures included in the BPriS method could enhance granularity in derived work items list and better project planning. Future improvements for the BPriS method could be in different aspects, to resolve limitations and improve usability. Some of the BPriS method improvement directions could be as follows:  
* Development of software patterns and work items patterns that are related to other types of software features and activities. * Development of more precise software functional patterns and work items list patterns, according to different technologies. Technology dependent software functional patterns and work items list patterns are more refined in real word software projects. They have more precise granulation, which depends on particular technologies, programming languages, databases and tools. * Data stores (resources) with data records from business process models should be addressed for their structure, to obtain data substructures, which can be a basis to derive software functions to support coding tables. * \"Master--detail\" or other more complex data relationships should be considered in software development activities, since not all complex types of software could require the same number and complexity of activities in implementation. * Technology-dependent software functional patterns and work items list patterns could be obtained during software development. They can be collected by automated monitoring of different software projects to enable future software support.
## 2 MWDEX: Moodle Workshop Data EXtractor
### Security and Access Configuration  
As mentioned in Section 2.1, security is one of the main concerns of Moodle LMS administrators, which is why solutions based on plug-in installation are hardly ever implemented across different institutions. The use of web services allows administrators to remain in complete control of permissions and access levels of the different types of users based on roles or individual permissions. The different functions used by the web service include a series of required capabilities; in other words, if a user (or the role the user has in Moodle) does not have those capabilities, the functions, when called, will return an error (\"permission denied\") instead of the data requested. Because administrators by default have all the required capabilities, they only require a token to access the service; however, the teacher role generally assigned to instructors does not have the \"webservice:createtoken\" capability by default, and this might impede the use of MWDEX by instructors.  
There are three possible and valid solutions for this problem which include: (1) changing the teacher role to give the role the missing capability, which would grant all teachers in the LMS this capability and could be considered a potential security flaw; (2) creating a \"MWDEX teacher role\" as a clone of the teacher role with the only difference of the missing capability activated, and assign that role to all teachers using Moodle Workshops and MWDEX; in this case, the potential problem would be limited to the teachers with this new role; (3) creating a web services user; then create a web services role with the appropriate capabilities allowed (in this case, webservice:createtoken and webservice/rest:use) and assign it to the web services user as a system role. The web service user is then added as an authorized user and assigned a token; administrators can then assign the web services role only to the teachers who will be using the web service, effectively limiting access to the web service only to authorized users. The third option can be considered the most restrictive and secure, and therefore is the one recommended for the configuration of the web service.
# Iterative Receiver Design for the Estimation of Gaussian Samples in Impulsive Noise +
## 4 Signal Estimation
### Lower FG Half: BCJR  
As mentioned earlier, the message \(P_{d}(i_{K})\) in (22) is the result of a convolution between two Gaussian pdfs, one provided by the channel observation, \(p(y_{k}\mid s_{k},i_{k})\), and the other, \(p_{d}(s_{k})\), by the upper FG half:  
\[P_{d}(i_{k}) = \int p_{d}(s_{k})p(y_{k}\mid s_{k},i_{k})ds_{k} \tag{37}\] \[= \int g(s_{k};\hat{\eta}_{k},\hat{\gamma}_{k}^{2})g(y_{k};s_{k}, \sigma_{i,k}^{2})ds_{k}\] \[= g(y_{k};\hat{\eta}_{k},\hat{\gamma}_{k}^{2}+\sigma_{i,k}^{2})\quad.\]  
As discussed in Section 2.2, \(\sigma_{i,k}^{2}\) takes \(M\) different values, each in a one-to-one correspondence with an impulsive noise channel state. According to (15) and (37), the forward and backward recursions (19) and (20), along the bottom line of the FG edges, are:  
\[P_{f}(i_{k}) = \sum_{i_{k-1}}P(i_{k}\mid i_{k-1})P_{f}(i_{k-1})g(y_{k-1};\hat{ \eta}_{k-1},\hat{\gamma}_{k-1}^{2}+\sigma_{i,k-1}^{2}) \tag{38}\] \[P_{b}(i_{k}) = \sum_{i_{k+1}}P(i_{k+1}\mid i_{k})P_{b}(i_{k+1})g(y_{k+1};\hat{ \eta}_{k+1},\hat{\gamma}_{k+1}^{2}+\sigma_{i,k+1}^{2})\quad. \tag{39}\]  
The above equations form the well-known MAP symbol detection algorithm known as BCJR [25] and more generally referred to as the forward-backward algorithm [18].  
For each variable node \(i_{k}\), the product of (38) and (39) is the message, here named \(P_{u}(i_{k})\), that is sent upward to the factor node \(p(y_{k}\mid s_{k},i_{k})\),  
\[P_{u}(i_{k})=P_{f}(i_{k})P_{b}(i_{k})\quad, \tag{40}\]and can be regarded as a provisional estimated PMF for the \(M\) possible different values of the impulsive noise channel state.
# Compression of Vehicle Trajectories with a Variational Autoencoder
## 4 Methodology
### Encoding and Decoding Performance  
In Figure 5, the CVAE encoding performance is illustrated on three maneuvers. From top to bottom: Reconstruction with 10-, 6-, and 4-dimensional latent space. From left to right: The same trajectory of a lane-keeping and lane changing on the right and left. The red curve is the ground truth trajectory, while the green one is the most probable reconstruction given the latent distribution. With blue lines, several reconstructions are plotted using 100 different context vectors sampled from the latent distribution. The transparency of the line is proportional to its likelihood. The horizontal axis is the longitudinal \(y\) coordinate of the vehicle's trajectory, and the vertical is the lateral \(x\) coordinate. Under the trajectory graphs, the hidden state vector is plotted.  
It can be seen that, by reducing the dimension, the reconstruction ability deteriorates, which can also be observed in the values in Table 3. However, this does not have such a significant effect on the classification, see Table 5.
## 4 Proposed Path and Shape Planning Method
### Obstacle Waypoints Detection  
After defining boundary waypoints, the global planner will auto-define the waypoints near the obstacles in map according to Algorithm 2. Note that the obstacles locating randomly in the map have the arbitrary shapes. Specifically, on the horizontal line \(y=y_{n}^{bl}\) which connects \(W_{n}^{bl}\) and \(W_{n}^{br}\) the location from the first free space pixel on the left side of obstacle pixel with a distance of \(w_{m}/2\) will be selected as the location of left obstacle waypoint \(W_{n}(ol_{i})\) and the location from the first free space pixel on the right side of obstacles with a distance of \(w_{m}/2\) will be defined as the location of right obstacle waypoint \(W_{n}(ol_{i})\).  
Figure 11: Left and right boundary waypoints estimating.  
```
1:functionGetLeftObstacleWaypoints(map \(M\), boundary waypoints)
2:if there is no right obstacle waypoint on line \(y=y_{n}^{hl}\)then
3:initial\(W_{n}(ol_{i})\leftarrow(x_{ni}^{ol},y_{ni}^{ol})\), \(x_{ni}^{ol}\gets x_{n}^{hl}\), \(y_{ni}^{ol}\gets y_{n}^{hl}\), \(i\gets 0\)
4:else
5:initial\(W_{n}(ol_{i})\leftarrow(x_{ni}^{ol},y_{ni}^{ol})\), \(x_{ni}^{ol}\gets x_{n(i-1)}^{or}\), \(y_{ni}^{ol}\gets y_{n(i-1)}^{or}\), \(i\gets 0\)
6:endif
7:while pixel \((x_{ni}^{ol}+i,y_{ni}^{ol})\) is the free space pixel \(M(x_{ni}^{ol},y_{ni}^{ol})=0\)do
8:\(i\gets i+1\)
9:endwhile
10:if\(x_{ni}^{ol}+i<x_{ni}^{br}\)then
11: the left obstacle waypoint \(W_{n}(ol_{i})\leftarrow(x_{ni}^{ol}+i-w_{r}/2,y_{ni}^{ol})\)
12:elseif there is no right obstacle waypoint on line \(y=y_{n}^{hl}\)then
13: no left obstacle waypoint on line \(y=y_{n}^{hl}\)then
14:endelseif
15:endif
16:functionGetRightObstacleWaypoints(map \(M\), boundary waypoints, left obstacle waypoint \(W_{n}(ol_{i})\))
17:initial\(W_{n}(or_{i})\leftarrow(x_{ni}^{or},y_{ni}^{or})\), \(x_{ni}^{or}\gets x_{ni}^{ol}+w_{r}/2\), \(y_{ni}^{or}\gets y_{n}^{hl}\), \(i\gets 0\)
18:while pixel \((x_{ni}^{or}+i,y_{ni}^{or})\) is the obstacle pixel \(M(x_{ni}^{ol},x_{ni}^{or})=100\) or obstacle pixel \(M(x_{ni}^{ol},x_{ni}^{or})=-1\)do
19:\(i\gets i+1\)
20:endwhile
21:if\(x_{ni}^{or}+i<x_{ni}^{br}\)then
22: the right obstacle waypoint \(W_{n}(or_{i})\leftarrow(x_{ni}^{or}+i+w_{r}/2,y_{ni}^{or})\)
23:elseif there is no left obstacle waypoint on line \(y=y_{n}^{hl}\)then
24: no right obstacle waypoint on line \(y=y_{n}^{hl}\)then
25:endelseif
26:endif
27:endfunctionGetRightObstacleWaypoints
28:return left obstacle waypoints, right obstacle waypoints
29:endfunctionGetLeftObstacleWaypoints
```  
**Algorithm 2** Determination of obstacle waypoints
## 3 Normalized Difference Canopy Shadow Index  
### Response of Red Edge and NDVI to Vegetation Shadows  
The RE's locations for both tree species were distributed around a wavelength of 710 nm regardless of the proportion of illumination to shadow, and its slopes and reflectances changed by a large margin. Correlations between RE's reflectances and slopes were drawn correspondingly in Figure 4 using 80 samples from a single tree species and all 160 samples together.  
Figure 4: Scatterplot of RE’s reflectance and slope for tree species. (**a**) Only _Pinus_. (**b**) Only **Larix**. (**c**) Both species.  
The \(R^{2}\) from the three charts all reached beyond 0.850 (\(p<0.01\)), which indicates a good linear correlation between the two parameters of the RE in either a single species or both species combined. Figure 3 shows that there is a positive correlation between the illumination proportion and the reflectance spectrum, which is especially distinctive around the red-NIR bands. Therefore, the RE's reflectance is easily determined and can differentiate the illumination/shadow proportion of a vegetation canopy quantitatively and without regard for the tree species.  
The NDVI is a classic and widely used vegetation indicator that is applied to detect vegetation growth and partially eliminate the influence of radiation error [45]. The value of the index is closely related to transpiration, photosynthesis, interception of sunlight, and the net primary productivity of the surface [46; 47; 48].  
Correlations between the RE's reflectance and the NDVI are shown in Figure 5 using 80 samples from each species. Standard deviation results of _Pinus_ (0.0458) and _Larix_ (0.0352) indicate that NDVI values are stable in a relatively narrow range without being disturbed by changes of RE's reflectance, and can differentiate the shadow proportion of vegetation canopy quantitatively. Therefore, NDVI is suitable for constructing a new, shadow-related vegetation index. It is rarely influenced by differing proportions of canopy shadow, guarantees a stable value for vegetation morphological characteristics, and is able to distinguish green vegetation individually from a soil background.
## 6 Conclusions  
This paper introduces a novel identification and adaptive control algorithm tailored for FJRs, aimed at enhancing NN training efficiency and trajectory tracking accuracy. We begin by applying the SPT method to decompose the intricate high-order system into two easily-managed low-order subsystems. This simplifies controller design by focusing on these reduced-order components instead of the full high-order system. To ensure desired transient and steady-state characteristics, we integrate the PPC technique into the slow subsystem's controller design. For faster convergence, continuous terminal sliding mode controllers are developed for both slow and fast subsystems. Additionally, we boost identification performance by merging the tracking error with the conventional OBE algorithm, creating a novel composite-learning OBE-based learning rule. Simulations reveal that using the proposed identification and control algorithm leads to closer tracking of reference states, quicker convergence of tracking errors to zero, and smaller steady-state tracking errors. This improved performance is attributed to the capture of model uncertainties resulting from system order reduction, as tracking errors are also utilized to train NN weights. Furthermore, NN training speed is accelerated. The immediate next step involves implementing the proposed algorithm on a real multi-link FJR to further validate its practical efficacy. Given that many practical FJR systems experience input saturation nonlinearity due to limited motor drive capacity, investigating SPT-based PPC under input saturation constraints is warranted. Lastly, while this paper establishes the closed-loop system's boundedness, proving its finite-time convergence would be a valuable future extension. Figure 17: Control signal for joint 2. **Author Contributions:** Conceptualization, D.Z. and X.R. ; methodology, D.Z. and K.G. ; software, X.L. and D.Z. ; validation, D.Z., K.G. and X.R. ; formal analysis, X.L., D.Z. and X.R. ; investigation, X.L. and D.Z. ; resources, K.G. and X.R. ; data curation, X.L. and D.Z. ; writing--original draft preparation, X.L. ; writing--review and editing, D.Z., K.G. and X.R. ; visualization, X.L. ; supervision, D.Z. ; project administration, X.R. ; funding acquisition, D.Z. and K.G. All authors have read and agreed to the published version of the manuscript. **Funding:** This work is supported in part by the National Nature Science Foundation under Grant No. 62103051, 52205035, in part by the the National Key R&D Program of China under Grant No. 2023 YFB4703900, and in part by the Beijing Institute of Technology Research Fund Program for Young Scholars. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author due to privacy. **Conflicts of Interest:** The authors declare no conflicts of interest. **Notations**  
The following notations are used in this manuscript:
# Learnable Nonlocal Contrastive Network for Single Image Super-Resolution
## 3 Method
### Network Structure  
Our proposed LNLCN consisted of five main modules: a shallow feature extraction module, a deep feature extraction module based on the learnable nonlocally residual group (LNLRG), an upsampling module, a reconstruction module, and an adaptive target generator module. We represented by \(I_{LR}\) and \(I_{SR}\) the input image and output reconstructed image of LNLCN, respectively. First, we extracted feature information on the input \(I_{LR}\) through a convolutional layer:  
\[F_{0}=H_{SF}(I_{LR}) \tag{1}\]  
where \(H_{SF}(*)\) represents the operation of a convolution layer. Then, the obtained image features \(F_{0}\) were input into the learnable nonlocally residual group (LNLRG) to extract image depth features:  
\[F_{DF}=H_{LNLRG}(F_{0}) \tag{2}\]  
where \(H_{LNLRG}(*)\) represents our proposed learnable nonlocally residual group (LNLRG), which consists of G deep feature fusion attention group (DFFAG) models that focus on  
Figure 1: Architecture of the proposed learnable nonlocal contrastive network (LNLCN). image-related information. After that, we upscaled the resulting deep feature \(F_{DF}\) through the upsampling module:  
\[F_{UP}=H_{UP}(F_{DF}) \tag{3}\]  
where \(H_{UP}(*)\) and \(F_{UP}\) represent the upsampling module and the upsampled features, respectively. Some effective upsampling modules, such as transposed convolution [40] and subpixel convolution [25], have been shown to improve performance without increasing the computational complexity and, thus, are more suitable for deep network models. After that, we reconstructed \(F_{UP}\) through a convolutional layer to obtain an SR image:  
\[I_{SR}=H_{SR}(F_{UP})=H_{LNLCN}(I_{LR}) \tag{4}\]  
where \(H_{SR}(*)\) and \(H_{LNLCN}(*)\) represent the features of the reconstruction module and the proposed LNLCN, respectively. After that, we optimized the model with a loss function. We used the same L1 loss as in previous work (SAN) [20]. Specifically, for a given training set \(\left\{I_{LR}^{i},I_{HR}^{i}\right\}_{i=1}^{N}\) containing N pairs of LR images \(I_{LR}\) to be processed and corresponding original HR images \(I_{HR}\), the ultimate goal of model training was to minimize the L1 loss function:  
\[L(\Theta)=\frac{1}{N}\underset{i=1}{\overset{N}{\sum}}\parallel H_{LNLCN} \Big{(}I_{LR}^{i}\Big{)}-I_{HR}^{i}\parallel_{1} \tag{5}\]  
where \(\Theta\) represents the parameter set of the LNLCN. In the optimization of the loss function, we used the stochastic gradient descent algorithm to ensure the stability of the training. After the initial training of the LNLCA network, an adaptive target \(\widetilde{y_{i}}\) was generated through the ATG network:  
\[\widetilde{y_{i}}=ATG(I_{HR},H_{LNLCN}(I_{LR})) \tag{6}\]  
where \(ATG(*)\) and \(H_{LNLCN}(I_{LR})\) represent the adaptive target generation network and the current prediction output of the LNLCN, respectively. The ATG network affinely transformed \(I_{HR}\) to \(H_{Lthan}(I_{LR})\) within an acceptable range.
# Wavelength-Dependent Nonlinear Absorption in Palladium Nanoparticles
## 2 Materials and Methods  
PdNPs were prepared using a water bath method [24]. Firstly, 10 mL 2 mmol/L Pd(NO3)2\(\cdot\)2H2O solution and 10 mL 2 mmol/L H4[Si(W3O10)4]-xH2O solution were mixed. 1 mL C3H8O was added. 0.2 g polyvinylpyrrolidone (PVP) was added. Aging time was 30 min, and during aging, the color of the solution did not change significantly. Secondly, the water bath was heated at 60 \({}^{\circ}\)C and stirred by magnetic force. The reaction lasted for about 12 min until the solution turned grey and black. Lastly, after centrifugation, the precipitates were washed with dilute NH3\(\cdot\)H2O, then washed with ultrapure water and absolute ethanol several times, and then dried in a vacuum drying incubator at 60 \({}^{\circ}\)C for 6 h to prepare the PdNPs.  
The PdNPs were observed by scanning electron microscopy (SEM). The ultraviolet-visible absorption spectrum of the PdNPs was measured by using Ocean Optical USB 4000 spectrometer. The nonlinear absorption properties of the PdNPs were studied by the typical open aperture (OA) Z-scan technique [25]. In Z-scan measurement, a nanosecond Nd:YAG laser (6 ns pulse width operating at 10 Hz) and optical parametric oscillator (OPO) were used to generate laser pulses with tunable wavelengths. The spatial distribution of the pulses was close to a Gaussian distribution. The PdNP ethanol solution was loaded into a 2 mm quartz cuvette. The linear transmission of the sample was 40% at 500 nm. The waist radius of the laser beam was measured to be \(\omega_{0}\) = 50 \(\upmu\)m with the blade method. The sample was mounted on a mobile platform and was moved along the beam axis (Z-axis). The incident and transmitted laser pulse energies for each \(z\) position were recorded by a computer.