esa-sceva/satcom-chunk-collection huggingface dataset:
# Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
## 5 Machine Learning Models for Identification of the LOS, NLOS, and MP Conditions  
Three machine learning models (SVM, RF, and MLP) were evaluated in this paper. SVM was regarded as a baseline model in the evaluation since it was the most commonly and frequently used model for the UWB-based identification of NLOS conditions in the literature [9; 10; 17; 21]. The configuration and setup for each classifier are discussed in the subsequent subsections.  
The training and test times of each classifier reported in this section were based on a single concurrent CPU core without using any parallel computing devices such as the GPU. The evaluation was done on the same machine for all classifiers. The reported results for all classifiers in this section (SVM in Section 5.1, RF in Section 5.2, and MLP in Section 5.3) were based on 10 iterations of randomly splitting the measured training, validation, and test data. The training and test datasets used in this section were the random splitting of the data collected in Figure 2(b). This explained the test datasets collected in Figure 2(c), which were used only in the evaluation results presented in Section 7. The extracted features used for all classifiers in this section were based on the discussion and selection observed in Section 6. The reported training and test times per sample (mean value) for each classifier were estimated in two steps. First, we estimated the total amount of time it took for the whole dataset in the training and test phases using the corresponding training and test dataset. Then, the measured time was divided by the total number of samples to get the mean value per sample.  
Generally, several parameters in the ML classifiers were tuned to achieve the optimized results. Moreover, each classifier had its specific hyper-parameters, which were not compatible with one another. Therefore, a direct comparison using exactly the same parameters for all classifiers was impossible. For the sake of simplicity and better representation of the results, the comparison is done by choosing the most important and influential parameters for each classifier in this section. This implied that the kernel type was chosen for SVM, and the number of decision trees in the forest was selected for RF. For MLP, the number of hidden layers including the total number of neurons in each layer was evaluated to choose the best option for the given problem.  
For the reproducible results, the parameters for each classifier such as the activation function, optimizer, earlier stopping criteria for the training, learning rate, etc., were based on the default setting of the scikit-learn [26] library if nothing is explicitly mentioned in the following. The applied stable version of the scikit-learn library was 0.23.1 at the time of writing this paper.
## 3 Mechanically Tunable PhC Cavity Sensors
### Sensing Applications of Mechanically Tunable PhC Cavities
#### 3.2.3 Mode-Splitting Based Coupled PhC Sensors
For sensing application, a numerical study on the double-layered PhC cavity was investigated under applied stress induced by an atomic force microscope (AFM) tip [12; 102], as shown in Figure 12a. Two InGaAsPhC microcavities were vertically coupled with a 400-nm air gap, and their mode properties and optical spectrum response were modulated when this gap varied with the structural bending of PhC cavity. The detectable stress limit was estimated to be as small as 0.95 nN with an air gap of 165 nm in the presence of point AFM force. Experimentally, an on-chip displacement sensor was realized based on the  
Figure 10: Schematics of mechanically tunable coupled PCN cavities: (**a**) doubly-coupled PCN cavity; (**b**) in-plane gap tuning [49; 50; 51; 52]; (**c**) in-plane lateral center offset tuning [53; 54]; (**d**) out-of-plane rotation [55]; (**e**) out-of-plane bending [56]. double-membrane PhC slab integrated with electro-optical readout, as presented in Figure 12b [10; 103]. The performance of the proposed displacement sensor was also characterized by nanomechanical actuation using a commercial AFM tip. It was demonstrated that the sensor had a sensitivity of 0.17 nm/nm (wavelength/displacement) and a displacement noise floor of 7 fm/\(\sqrt{\text{Hz}}\).
# Synchronizing TSN Devices via 802.1 AS over 5G Networks
## 2 Time-Sensitive Networking
### Bounded Low Latency  
Ensuring that data packets reach the destination within the application specified time requirements is of utmost importance in real-time networks. TSN makes use of VLAN Tagging and implements extensions to IEEE 802.1Q standard.  
To achieve guaranteed end-to-end latency, traffic shaping standards are used to reduce the buffering of frames in bridge devices. A Credit-Based Shaping (CBS) mechanism is defined in IEEE 802.1 Qav [2], which allows bandwidth reservation for up to eight distinct traffic classes. Each traffic queue is assigned a class and is associated with an idleSlope parameter that adds credits to the queue, which are required to transmit frames. Depending on the queue class and the number of frames in the queue, credits are increased or decreased and depending on the available credits; frames from the queue are forwarded.  
A Time-Aware Scheduling mechanism is defined in IEEE 802.1 Qbv [3]. Time-Aware Shaper (TAS) adds a transmission gate to each queue, which opens and closes, allowing the transmission of frames. The state of the gates is controlled by a Gate Control List (GCL), which is computed by CNC. The entries in the GCL are set as the current configuration for a specified time, beginning with the first entry. After all entries have been applied, the cycle is repeated.  
Frame pre-emption is defined in IEEE 802.1 Qbu, allowing the interruption of low-priority frame transmission if a high-priority frame is received for forwarding.
## 3 Methodology
### Discovery Phase  
The discovery phase starts with generating event logs from the activities identified at the construction site. For each of the moving agents on the construction site (e.g., dump trucks), one activity profile is created. Figure 4 demonstrates an example of an activity profile. Activities and their associated durations are calculated aligning with the time of occurrence. For example, at 6:32, the dump truck was loading for about 3 min.  
The operation that is chosen for this study is moving earth from one location of the job site to another with the help of an excavator and dump truck. This operation is cyclical in nature and follows four major distinct activities for the dump truck: load, haul, dump, and return. In addition to that, the dump truck might have to wait in the queue before loading or dumping. Each cycle of each dump truck is considered a case, and each activity within that cycle is considered an event. The activity profile shown in Figure 4 is converted into an event log following the times and the sequence of the events (i.e., activities). Each cycle or case can be represented by a trace, and the multiset of the traces is represented by the event log. A sample trace can be expressed by \(t=\langle\mathit{wait\ to\ load,\ load,\ haul,\ wait\ to\ dump,\ dump,\ return}\rangle\in T\) refers to five events belonging to the same case. If \(A\) is the universe of activities, a trace \(t\in A*\) is the sequence of activities. \(T=A*\) is the universe of traces. \(\mathcal{L}=\mathbb{B}(T)\) is the universe of event logs. \(l\in\mathcal{L}\) is a multiset of observed traces. \(\overset{\sim}{I}=\{t\in l\}\) is a set of traces appearing in the \(l\in\mathcal{L}\).  
Once the event log is generated from the activities, process discovery algorithms (e.g., \(\alpha\)-algorithm, heuristic miner) are used to find discover the underlying behavior of the event log. Details of these algorithms are explained in [PERSON] et al. [4]. The open-source process mining software, ProM is used to discover the process from event logs. Different parameters are tested until the discovered process reflects real-world operations accurately. Once the underlying behavior of the operation is known, the discrete-event simulation (DES) technique is used to generate a model of the operation. In this study, a discrete-event simulation tool, JStrobe is used to develop the simulation model. The first couple of cycles  
Figure 4: Activity profile of a dump truck.  
of the operation is used to determine the duration of each activity. This model is used as a baseline to update while more recent data from the field are generated.
# Kinematic Tripod (K3P): A New Kinematic Algorithm for Gait Pattern Generation
## 2 Related Work  
Legged robots perform different gait patterns depending on the desired horizontal speed and stability criteria [5; 6; 7; 8]. Figure 1a shows a simplified view of the robot limb and the two phases of the gait. Complementarily, Figure 1b shows a complete gait cycle for what is known as static fast gait; as it can be observed, a minimum of three limbs (a tripod) support the robot during walking. The ratio of duration of the support phase to the total cycle duration defines the duty factor \(\beta\) of a gait cycle [9], Figure 1b displays gait cycle for a duty factor \(\beta=0.5\). Medium speed gaits (or ripple gait) allow for two legs on opposite sides of the robot to be in the swing phase. In slow gait (or tetrapod gait), only one limb at a time performs the swing phase while the rest support the robot; therefore, it is the most stable of the three gaits [10]. So, the problem of synthesizing a gait pattern consists of defining the best sequence of movements for all the robot limbs, where each limb features from one [11; 12] up to four degrees of freedom [10; 13].  
For the purpose of this work, we have classified the related works according to the use of precomputed, heuristic, or bio-inspired approaches to generate the gait.  
Figure 1: The gait cycle of an hexapod robot. (**a**) Side view of a leg performing a gait cycle: During the swing phase, the end side of the limb describes a curve or a triangular trajectory. Every DoF is marked with a circle; thus, the limb displayed above has 3 DoF. (**b**) Fast gait diagram: The beginning and end of the lines, marked with a circle, correspond to the landing and lifting of every limb, respectively. Every limb is marked as a combination of (T)op, (B)ottom, (M)iddle, (L)left, and (R)ight, according to its position. When the surface is flat and every trajectory for the legs is precomputed, this gait sequence is sufficient [10].
# A Novel Shadow Removal Method Based upon Color Transfer and Color Tuning in UAV Imaging
## 5 Conclusions  
In the proposed work, a methodology for cast shadows and self-shadows removal was presented. The proposed approach offers a tool based on color transfer for color correction in shadowed regions in urban aerial scenes captured with UAV. The presented work was tested under different urban scenes containing roads, concrete sidewalks, and green areas, where scenes presented different percentages of shadows were transformed into scenes that presented different darkness levels, and texture features were also considered. During the qualitative analysis, the advantages that this work shows over the other tested methods were demonstrated. Despite preserving shadow boundaries, the color consistency and texture preservation provided visually accurate results; this is mainly noticeable in vegetation, road, and sidewalk textures, which were successfully conserved. Likewise, according to the SSDI results, the proposed method provided far better results than the other tested methods in all the study cases in this work, which proves its accuracy. The qualitative and quantitative results validate this work as a valuable and affordable tool in aerial urban areas shadow removal tasks. Additionally, this methodology is helpful as a preprocessing step to execute remote sensing, pattern recognition, and image segmentation tasks. Further works under this topic would focus on enhancing the quality of shadow removal results in terms of shadow boundaries since it still involves a challenging task. Likewise, in future works, photogrammetric processing of the corrected images will be executed for specific applications.  
Conceptualization, G.A.-R., R.A.O.-R. and L.A.M.-H.; methodology, G.A.-R.; software, G.A.-R., M.A.G.-R. and F.J.S.-M.; validation, G.A.-R.; formal analysis, R.A.O.-R.; investigation, G.-A.-R.; resources, G.-A.-R. and L.A.M.-H.; data curation, G.A.-R.; writing--original draft preparation, G.A.-R.; writing--review and editing, R.A.O.-R., L.A.M.-H. and F.J.S.-M.; visualization, G.A.-R. and F.J.S.-M.; supervision, L.A.M.-H. and R.A.O.-R.; project administration, L.A.M.-H.; fund ing acquisition, G.A.-R., R.A.O.-R., L.A.M.-H. and M.A.G.-R. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the Mexican National Council for Science and Technology (CONACYT) through [PERSON] Ph.D., grant number 666566/487077.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Path Algorithms for Contact Sequence Temporal Graphs +
## 2 Materials and Methods
### TRG Data Structures
#### 2.1.1 TRG_[PERSON]  
Consider the edge \((u,v,t,w)\) in a contact sequence temporal graph. One can use this edge to depart vertex \(u\) at time \(t\) and arrive at vertex \(v\) at time \(t+w\). Let \(T_{out}(u)\) be the set of distinct times one can depart from \(u\) and \(T_{in}(u)\) be the set of distinct times one can enter \(u\). Equivalently, \(T_{out}(u)\) and \(T_{in}(u)\) can be defined as the sets of distinct timestamps on the edges of the forms \((u,*,t,w)\) and \((*,u,t,w)\), respectively. Given a graph \(G=(V,E)\), [PERSON] et al. [8] define a transformed graph \(G^{\prime}=(V^{\prime}E^{\prime})\). For each vertex \(u\) in \(G\), there are two sets of vertices in \(G^{\prime}\)  
\[V_{in}(u)=\{u^{\prime}=(u,t)|t\in T_{in}(u)\},\]  
\[V_{out}(u)=\{u^{\prime}=(u,t)|t\in T_{out}(u)\}.\]  
So, \(V^{\prime}=\bigcup_{u}\{V_{in}(u)\cup V_{out}(u)\}\). The process by which the edge set \(E^{\prime}\) is formed is described below.  
1. For each \(u\in V\), sort the vertices in \(V_{in}(u)\) in ascending order of the in-time (arrival time). Connect each vertex to the next one (in the sorted order) using a zero-weight directed edge. Similarly, sort the vertices in \(V_{out}(u)\) in ascending order of the out-time (departure time) and link each vertex to the next one using a zero-weight directed edge.
2. For each \(u\in V\) iterate over \(V_{in}(u)\) in descending order of \(t\). For each \((u,t_{1})\in V_{in}(u)\), determine the minimum time-stamp \(t_{2}\) in \(T_{out}(u)\) for which the inequality \(t_{2}\geq t_{1}\) holds. Check for an already existing edge from any vertex in \(V_{in}(u)\) to \((u,t_{2})\in V_{out}(u)\). If such an edge does not exist, add a directed one from \((u,t_{1})\) to \((u,t_{2})\) with a weight equal to 0.
3. For each edge \(e=(u,v,t,w)\in E\), add a directed edge with weight \(w\) from \((u,t)\in V_{out}(u)\) to \((v,t+w)\in V_{in}(v)\).  
Figure 1c gives the TRG_Wu transformation of the graph in Figure 1a. We can easily see that for every time-respecting path in \(G\), there is a path in \(G^{\prime}\) and vice versa. Therefore, we can use the classic graph algorithms such as depth-first and breadth-first search on \(G^{\prime}\) to easily solve a group of problems in \(G\). An example is finding all nodes \(v\) that are reachable from \(u\) using time-respecting paths. Moreover, using \(TRG\_Wu(G)\) over \(OSE(G)\) results in faster solutions (especially when the number of reachable vertices is much less than \(|V|\)). From the described construction for \(G^{\prime}\), we see that  
\[|V^{\prime}|=\sum_{u\in V}\left(|T_{in}(u)|+|T_{out}(u)|\right)\leq 2|E| \tag{1}\]  
and  
\[\begin{split}|E^{\prime}|\leq\sum_{u\in V}\left(|T_{in}(u)|-1 \right)+\sum_{u\in V}\left(|T_{out}(u)|-1\right)\\ +\sum_{u\in V}\min\{|T_{in}(u)|,|T_{out}(u)|\}+|E|\\ =\sum_{u\in V}\left(|T_{in}(u)|+|T_{out}(u)|+\min\{|T_{in}(u)|,| T_{out}(u)|\}\right)\\ -2|V|+|E|\leq 4|E|-2|V|.\end{split} \tag{2}\]
# FRAMUX-EV: A Framework for Evaluating User Experience in Agile Software Development
## 7 Discussions
### Challenges in Applying FRAMUX-EV  
The integration of FRAMUX-EV in agile projects can present some significant challenges for different work teams. Introducing new UX events and UX artifacts requires not only adjustments to well-established workflows but also the involvement of the entire team, which can be difficult to guarantee. Teams that are familiar with their current way of working may resist the changes required to adapt to this new proposal. In addition, the time required for these UX activities, in particular the weekly UX meeting and weekly user meeting, could overload already tight sprint schedules, raising questions about their feasibility in agile projects with small iterations.  
The framework's adaptability to different team compositions is another critical factor for its success. FRAMUX-EV must work effectively across different team structures, from those without dedicated UX roles to teams that include multiple UX roles. However, this flexibility introduces a learning curve, as teams will need time to understand and properly apply the UX artifacts and UX events. Furthermore, it can be challenging for teams without UX roles to balance UX and development tasks in the backlog and sprint planning, as developers need to make sure UX tasks get enough focus but without slowing down development activities.  
On the other hand, creating and maintaining up-to-date UX artifacts, such as the UX backlog, the UX evaluation repository, and the UX design system, requires the team's continuous dedication to ensure their relevance and usefulness throughout the project lifecycle. This effort is essential to prevent UX artifacts from becoming outdated given the evolving needs of the project. Finally, user involvement is crucial to the success of FRAMUX-EV; however, obtaining and analyzing user feedback without delaying other activities could be a problem for some teams.
# Experimental Assessment of the Spatial and Temporal Distribution of Non-Contact Tonometer Airflows
## 3 Results
### Comparison between the Central CorvisST and ORA Airflow Pressures  
The ORA airflow measurement commenced at 0.693 ms and lasted for 23.061 ms, 2.5% longer than the CorvisST airflow. The maximum airflow pressure for the ORA was reached 1.155 ms before the CorvisST maximum, and it was 4.9% lower and more curved (skewness: CorvisST 0.549, ORA 0.657; kurtosis: CorvisST \(-\)1.322, ORA \(-\)1.113). The duration of the maximum pressure was shorter with the ORA (1.182 \(\pm\) 0.1659 ms) when compared to the CorvisST (1.317 \(\pm\) 0.1116 ms). The mean difference between the CorvisST and ORA airflow pressures was 2.6 \(\pm\) 4.07 mmHg (95%CI from \(-\)5.5 to 10.6 mmHg), Figure 10. The FWHM for the maximum airflow pressure over time for the ORA was found to be 9.5 ms, whereas for the CorvisST it was 10.9 ms.
# Application of HY-2B Satellite Data to Retrieve Snow Depth on Antarctic Sea Ice
## 4 Results and Discussion
### Comparison of HY-2B SMR Snow Depth with GCOM-W1 AMSR-2 Snow Depth Product
Considering the underestimation of GCMW1 snow depth product on Antarctic sea ice [19; 39], the HY-2B SMR retrieved snow depth, which is thicker than the GCOMM1 snow depth in this study, which is closer to the true snow depth situation. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sea Ice State** & **Antarctic Sea Area** & **MD/cm** & **RMSD/cm** \\ \hline \multirow{6}{*}{Spring (September to November)} & Bellingshausen and Amundsen seas & 6.4 & 11.8 \\  & Weddell Sea & 2.0 & 9.2 \\  & Southern India Ocean & 2.4 & 7.9 \\  & Southern Pacific Ocean & 2.1 & 8.4 \\  & Ross Sea & 1.5 & 8.0 \\ \hline \multirow{6}{*}{Summer (December to February)} & Bellingshausen and Amundsen seas & 12.1 & 21.8 \\  & Weddell Sea & 9.2 & 16.9 \\ \cline{1-1}  & Weddell Sea & 6.6 & 17.3 \\ \cline{1-1}  & Southern Pacific Ocean & 6.1 & 18.4 \\ \cline{1-1}  & Ross Sea & 9.7 & 17.5 \\ \hline \multirow{6}{*}{Autumn} & Bellingshausen and Amundsen seas & 5.0 & 13.1 \\ \cline{1-1}  & Weddell Sea & 0.9 & 9.4 \\ \cline{1-1}  & Southern India Ocean & 3.3 & 12.7 \\ \cline{1-1}  & Southern Pacific Ocean & 2.5 & 10.1 \\ \cline{1-1}  & Ross Sea & 0.9 & 7.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Differences between HY-2B SMR snow depth and GCOM-W1 AMSR-2 snow depth products across different Antarctic seas and different seasons in 2019. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sea Ice State** & **Month** & **MD/cm** & **RMSD/cm** \\ \hline \multirow{6}{*}{Ablation period} & November & 5.3 & 6.9 \\  & December & 9.3 & 14.8 \\  & January & 11.1 & 19.4 \\  & February & 5.9 & 16.9 \\  & March & 5.2 & 14.2 \\  & The whole ablation period & 5.0 & 14.4 \\ \hline \multirow{6}{*}{Growth period} & April & 2.4 & 10.0 \\  & May & 1.2 & 8.0 \\ \cline{1-1}  & June & 1.3 & 7.4 \\ \cline{1-1}  & July & 0.6 & 6.8 \\ \cline{1-1}  & August & 1.1 & 6.9 \\ \cline{1-1}  & September & 1.3 & 6.7 \\ \cline{1-1}  & October & 2.3 & 7.1 \\ \cline{1-1}  & The whole growth period & 1.0 & 7.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of snow depths retrieved from HY-2B SMR using different snow depth models against ship-based snow depth measurements in different months.
## 2 Sensor Platform LSI Network System
### Sensor Platform LSI
#### 2.2.4 Configuration  
The physical address is used to send commands to a specific sensor platform LSI to write its registers for configuration. In this study, all sensor platform LSIs were configured to operate in capacitive sensing mode. As shown in Figure 7, one data packet was configured to include 24 bytes of data: ID number (8 bits), sensing mode (8 bits), sensing data from the on-chip temperature sensor (10 bits), sensing data from capacitive channels (128 bits), other LSI status information (22 bits) and CRC code (16 bits). After 4B5B encoding and attaching a 9-bit preamble and a 5-bit stop code, one data packet consists of 254 bits. Assuming the frequency of the on-chip clock is 61 MHz and the data transmission speed is 61/64 MHz = 953.1 kHz, the length of one data packet is 266.5 \(\upmu\)s.  
Figure 6: Bus status with different function configurations: (**a**) with neither adaptation nor event-driven function; (**b**) with event-driven function; (**c**) with adaptation and event-driven functions.  
Figure 7: Structure and waveform of a data packet.
# Adsorption of Cadmium and Methylene Blue Using Highly Porous Carbon from Hemp Seeds
## 4 Results and Discussion
### Isotherm, Kinetics and Thermodynamic Studies
#### 4.3.2 Kinetic Models  
The rate at which methylene blue and cadmium are adsorbed from water is important. The rate was estimated to determine the efficiency of PHSs, CHS-500 and CHS-700 for water treatment. The sorption of cadmium and methylene blue by PHSs, CHS-500 and CHS-700 was fitted into the pseudo-first-order (PFO) and pseudo-second-order (PSO) models and intraparticle diffusion (IPD). The parameters in Table 2 show that the PSO model is a better fit for the cadmium and methylene blue uptake by PHSs, CHS-500 and CHS-700 than the PFO model. The R\({}^{2}\) indicates that values are closer to 1, suggesting that the intra-particle diffusion (IPD) model did not fit.