esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### RNA Isolation and mRNA Expression Analysis  
Total RNA from _M. japonicus_ was obtained using the RNAiso Plus reagent (Takara, Dalian, China) following the manufacturer's protocol. Recombinant DNase I (RNase free) (Takara, Kusatsu, Japan) treatment was used to eliminate contamination of genomic DNA. Integrity and quantity of the extracted RNA were checked using 0.8% agarose gel for electrophoresis and a Nano-Drop 1000 (Thermo Fisher Scientific, Foster City, CA, USA). cDNA synthesis was performed with 3 \(\upmu\)g of total RNA using the SuperScript(tm)III RT kit (Invitrogen, Foster City, CA, USA) following the manufacturer's protocol. After synthesis, the diluted cDNA (50-fold) was stored at \(-80\)\({}^{\circ}\)C.  
To evaluate VTG gene responses in multiple tissues, the total RNA was extracted from _M. japonicus_ in a seawater control. Quantitative real-time PCR (polymerase chain reaction, RT-PCR) was performed using the Exicycle(tm)96 (Bioneer, Daejeon, Korea) with the master mix (Bioneer, Daejeon, Korea). The glyceraldehyde-3-phosphate dehydrogenase (_Mj GAPDH_) gene of _M. japonicus_ was used as an internal reference [16]. The primer sequences were: _Mj_VTG forward 5\({}^{\prime}\)-CTGGGCTCTCCAGTTCTG-3\({}^{\prime}\); _Mj_VTG_ reverse 5\({}^{\prime}\)-CCACGTATGCCTTTTGGT-3\({}^{\prime}\); _Mj_GAPDH_ forward 5\({}^{\prime}\)-TGCTGATGCACCCATGTTTG-3\({}^{\prime}\); _Mj_GAPDH_ reverse 5\({}^{\prime}\)-AGGCCCTGACAATCTCAAAG-3\({}^{\prime}\). The size of the PCR product was 164 bp for the VTG gene and 147 bp for the GAPDH gene. The reaction mixture (20 \(\upmu\)L) contained 6 \(\upmu\)L of diluted cDNA, 10 \(\upmu\)L of 2x SYBR green dye (Bioneer, Daejeon, Korea), 0.5 \(\upmu\)L of forward and reverse primers (10 \(\upmu\)M), and 3.0 \(\upmu\)L of RNase-free water. The following RT-PCR cycle was used for amplification of the VTG gene: 94 \({}^{\circ}\)C for 40 s, followed by 38 cycles of 94 \({}^{\circ}\)C for 15 s, 53 \({}^{\circ}\)C for 30 s and 72 \({}^{\circ}\)C for 40 s. The Exicycle(tm)96 real time system program (version 3.54.8) was used for the verification of the RT-PCR baseline. The calculation for relative transcript levels was used by the 2\({}^{-\Delta\,\mathrm{Act}}\) method [32] and normalized with GAPDH.
## 2 Materials and Methods
### The Study Area  
The study area is located near Xishan, Taiyuan, Shanxi Province, People's Republic of China (Figure 2). The approximate coordinates in the geodetic reference system WGS84 are \(112^{\circ}27^{\prime}5.64^{\prime\prime}\) E and \(37^{\circ}52^{\prime}0.98^{\prime\prime}\) N. The region covers an area of approximately 0.5 km\({}^{2}\), with the highest point at 896 m and the lowest point at 840 m. The area features railways, factories, and low vegetation.  
Figure 1: Workflow of methods, starting with the route planning of a UAV campaign until the determination of DSM and orthomosaic accuracy (_RMSE_ and M3C2 distance).  
Figure 2: Study area with the GCP and checkpoint distributions. Projection coordinate system: WGS_1984_UTM_zone_49N; (**a**) DOM and (**b**) route map.  
Accurate GCP coordinates are required for georeferencing UAV images. Sixteen ground survey markers were deployed, according to different photogrammetric projects--some of them were used as the input of the BA process, and the rest were used as horizontal checkpoints for cross verification of the horizontal accuracy. All control points were as evenly distributed as possible in the study area. In addition, coordinates of 120 points were obtained for vertical accuracy analysis of the generated DSM. Compared to CPs for horizontal accuracy assessment, vertical CPs do not require accurate identification in the image. Aerial markers consisted of 70 \(\times\) 70 cm highly reflective red and yellow material fixed with nails to the ground, far away from high vegetation, buildings, and slopes. These markers were large enough to be identified in images and placed and measured prior to flight. A GNSS RTK receiver was used for field measurements. The receiver was connected to a virtual reference station and received differential signals through the network. Ten fixed solutions were recorded at each point, and the average was taken as the final result. Taking the average of the results of multiple measurements as the final result can exclude the influence of accidental factors and make the measurement results closer to the true value. At a control point marker, it takes about 3-5 min from deployment to the end of measurement. All coordinates were recorded in the WGS84 reference system. In the experiment, the means of the RTK coordinate residuals in the X, Y, and Z directions were 0.007, 0.006, and 0.012 m, respectively. The expected coordinate accuracy was higher than the spatial resolution of the UAV image (GSD about 1.7 cm). Figure 2 shows the position of GCPs and CPs relative to the study area.
# Multimodal Attention-Based Instruction-Following Part-Level Affordance Grounding
## 3 Method
### Vision and Language Encoders  
For multimodal tasks involving both vision and language, the two-tower model architecture has emerged as a widely adopted approach for effectively extracting features from each modality. Therefore, in our approach we employ two distinct backbones, a visual encoder and a language encode, to separately extract visual and textual features. The network inputs consist of an image, \(I\in\mathbb{R}^{FH\times FW_{i}\times C}(C=3)\), and a language instruction, \(T=\{w_{1},w_{2},\ldots,w_{t}\}\), where \(FH\), \(FW\), and \(C\) represent the height, width, and color channel number of the image, respectively, and \(t\) denotes the length of the instruction. The visual encoder processes the image input, while the language encoder handles the textual input.  
**Visual Features:** We adopt ResNet 50 [65] as our visual backbone, following the approach of many prior works in image segmentation and detection [55; 66]. ResNet 50 is known for its ability to effectively capture detailed multiscale visual features. The backbone produces multilevel visual features, \(Res_{i}\in\mathbb{R}^{FH_{i}\times FW_{i}\times C_{i}}\), where \(Res_{i}\) corresponds to a ResNet 50 feature and \(i\in{1,2,3,4}\) denotes the block index. Here, \(FH_{i}\) and \(FW_{i}\) represent the height and width of the feature map, respectively, and \(C_{i}\) indicates the dimension of the feature channels.  
**Text Features:** Language feature are extracted using BERT [67], a language encoder that leverages a bidirectional self-attention mechanism within transformer layers to generate rich linguistic representations. Given an instruction sentence, \(T=\{w_{1},w_{2},\ldots,w_{t}\}\), where \(w_{i}\) represents the \(i\)-th word with \(i=\{1,\ldots,t\}\), BERT outputs a final hidden state, \(F_{w}=\{f_{1},f_{2},\ldots,f_{t}\}\in\mathbb{R}^{t\times d}\), where \(d\) is the dimension of the word embedding. Each embedding vector, \(f_{i}\), corresponds to the contextual representation of the \(i\)-th word, \(w_{i}\), in the instruction.
# Robot Localisation Using UHF-RFID Tags: A Kalman Smoother Approach +
## 3 Materials and Methods  
A configuration of a unicycle robot with a state vector \(s=[x,\,y,\,\theta]^{T}\) has the following form:  
\[\begin{split}\dot{x}&=v\cos\theta\\ \dot{y}&=v\sin\theta\\ \dot{\theta}&=\omega,\end{split} \tag{1}\]  
where \(\theta\) is the orientation with respect to the \(X_{w}\) axis of the reference frame \(\langle W\rangle\), while \(v\) (the forward velocity) and \(\omega\) (the angular velocity) are the input variables. Assuming that the RFID readings are measured with period \(T_{s}\) and that the command variable \(v\) and \(\omega\) are approximately constant throughout the sampling period, it is possible to find the following discrete time Zero-order-Hold equivalent dynamics [40]  
\[\begin{split} x_{k+1}&=\begin{cases}x_{k}+v_{k}T_{s} \cos\theta_{k}&\text{if }\omega_{k}=0,\\ x_{k}+2\frac{v_{k}}{\omega_{k}}\sin(\frac{\omega_{k}}{2}T_{s})\cos(\theta_{k }+\frac{\omega_{k}}{2}T_{s})&\text{otherwise},\\ y_{k+1}&=\begin{cases}y_{k}+v_{k}T_{s}\sin\theta_{k}&\text{if } \omega_{k}=0,\\ y_{k}+2\frac{v_{k}}{\omega_{k}}\sin(\frac{\omega_{k}}{2}T_{s})\sin(\theta_{k}+ \frac{\omega_{k}}{2}T_{s})&\text{otherwise},\\ \theta_{k+1}&=\theta_{k}+\omega_{k}T_{s}.\end{cases}\end{split} \tag{2}\]  
where we made use of the simplified notation \(x_{k+1}=x((k+1)T_{s})\) (i.e., \(s_{k+1}=[x_{k+1},y_{k+1},\,\theta_{k+1}]^{T}\)).
## 3 Network Slicing Concepts
### Slice Mapping  
This module serves as the placement module for optimally selecting infrastructure resources to be used, taking into account the various requirements from other modules and network environment elements. The resource selection is performed based on the NST and resource availability. This module hosts a very important process that runs during the slice creation phase: the placement process. This process is responsible for optimally selecting the infrastructure resources to be used for a new slice, based on the slice requirements, as they are described in the NST, and the available resources of the infrastructure layer.
# Variable Neighborhood Search for the Two-Echelon Electric Vehicle Routing Problem with Time Windows
## 4 Solution Approach
### Initial Solution Construction
```
1: Assign customers to the nearest satellite
2: Determine the latest possible visiting time \((twl_{s})\) for each satellite \(s\in N_{S}\)
3: Construct first echelon routes using the savings heuristic
4:for each satellite \(s\)do
5: Create back-and-forth tours for each customer \(i\in N_{\mathcal{C}}^{s}\)\((s-i-s)\)
6:if the created tour is infeasible in terms of the battery constraints then
7: Insert a charging station using the greedy CS insertion operator (see Section 4.3.3)
8:if the tour is still infeasible then
9: Discard the tour and add the customer to the unvisited customer list \(L_{u}\)
10:endif
11:endif
12: Generate the savings list and sort it in descending order based on the savings values
13:while savings list is not empty do
14: Merge the two tours with the greatest savings value
15:if vehicle capacity or time window constraints are violated then
16: Discard the tour and remove the corresponding pair of customers from the savings list
17:else
18:if the merged tour is infeasible in terms of the battery constraint then
19: Insert a charging station with a minimum insertion cost
20:if the tour is still infeasible then
21: Discard the tour and remove the pair of customers from the savings list
22:else
23: Accept the merged tour and update the saving list
24:endif
25:endif
26:endif
27:endif
28:endif
29:endif
30: Insert all customers from \(L_{u}\) into the constructed tours using the greedy customer insertion operator (see Section 4.3.3)
31:endfor
```  
**Algorithm 1** Modified C&W Savings Heuristic for the 2E-EVRP-TW  
Finally, note that the same procedure is applied to construct routes for the large vehicles in the first echelon. In this case, all aspects related to batteries and charing stations are removed from the heuristic procedure.
## 3 Results and Discussion
### Elastic Constants  
The Young's modulus, bulk modulus, and Poisson's ratio were determined for the base material (ferriti+pearilitic microstructure) at room temperature and were 206.6 GPa, 81.26 GPa, and 0.273, respectively, which corresponds to literature data [41]. The measured [PERSON]'s moduli over the temperature range of 25-900 \({}^{\circ}\)C for the specimen with initial martensitic and ferritic-pearilitic microstructures are shown in Figure 7. Both the specimens undergo phase transformation between A\({}_{\text{c1}}\) and A\({}_{\text{c3}}\), which can be seen as a pronounced decrease in [PERSON]'s modulus above about 700 \({}^{\circ}\)C. [PERSON]'s modulus of austenite remains almost constant up to 900 \({}^{\circ}\)C.  
Figure 6: Stress–strain curves at different temperatures. * Different geometry of the specimens tested at room temperature (RT) and 300 \({}^{\circ}\)C.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **T in \({}^{\circ}\)C** & \(R_{\text{p0.2}}\) **in MPa** & \(R_{\text{m}}\) **in MPa** \\ \hline  & RT & 1290 & 1598 \\  & 300 & 940 & 1150 \\ (Tempered) Martensite & 400 & 770 & 960 \\  & 500 & 650 & 730 \\  & 600 & 280 & 340 \\ \hline  & 700 & 92 & 145 \\ Austenite & 800 & 77 & 110 \\  & 850 & 74 & 90 \\  & 900 & 37 & 52 \\ \hline \end{tabular}
\end{table}
Table 3: Yield stress and tensile strength of martensite and austenite at elevated temperatures.  
The furnace was purged with argon gas while heating up to reduce the oxidation of the samples at high temperatures. Practically the oxidation cannot be completely prevented, which leads to changes in the specimen dimensions and their mass during the experiment, which causes uncertainties in the calculated Young's modulus. Thickness and mass were also measured after the experiment to determine the thickness of the oxide layer. Overall, this effect can lead to an error of up to \(\pm\)4 GPa in \(E\). The same experiment was performed for a specimen with initial ferritic-peariltic microstructure in order to check for differences resulting from the two variants of the initial microstructure. It turned out that these differences were in range of the estimated uncertainty.
# Ultrasonic Through-Metal Communication Based on Deep-Learning-Assisted Echo Cancellation
## 2 Materials and Methods
### Echoing Problem
The impulse response in Figure 2 clearly indicates that the first multipath signal in the channel appeared at 7.97 us which was approximately the time it took for the ultrasound to travel through the 50 mm thick aluminum barrier. The speed of sound in aluminum is 6.27 km/s. The last detectable multipath signal arrived at 57.06 us, meaning that the delay spread \(D\) of the channel was approximately 49.09 us. According to Equation (3), \(F_{c}\) should be 20.37 kHz. Therefore, when a signal with a bandwidth larger than 20.37 kHz passes through the channel, severe intersymbol interference will appear. The intersymbol interference problem in RF communications is generally dealt with by the channel equalization method. Taking into account that channel equalization was considerably complex for  
Figure 3: Magnitude response of the 50 mm long aluminum channel with a pair of coaxially aligned ultrasonic transducers. an uncertain or unstable channel, in this study an elaborately trained DPRNN model was introduced into the communication system to perform the echo cancellation and hinder intersymbol interference.
## 3 The Proposed Algorithm
### Selection and Mutation
#### 3.6.3 Crossover Operation  
In addition to mutation, we implement a crossover operation to combine the characteristics of two parent movements to create offspring. This can be performed by averaging the parameters of the selected seeds:  
\[M_{k}^{\text{offspring}}=\frac{1}{2}(M_{i}+M_{j}) \tag{13}\]  
where \(M_{i}\) and \(M_{j}\) are two selected parent movements. The offspring inherit traits from both parents, which can lead to better candidate movements. For more details about this whole step please see Algorithm 4.  
```
1:Input: Candidate movements \(M_{k}\), MSE values \(MSE_{k}\), threshold \(T\)
2:Output: New population of movements \(M_{new}\)
3:Initialize \(M_{new}\leftarrow\emptyset\)
4:for each movement \(M_{k}\) in \(M_{k}\)do
5:if\(MSE_{k}<T\)then
6: Add \(M_{k}\) to the retained movements
7:endif
8:endfor
9:for each pair of retained movements \(M_{i}\), \(M_{j}\)do
10: Generate offspring movement: \[M_{k}^{\text{offspring}}=\frac{1}{2}(M_{i}+M_{j})\]
11: Add \(M_{k}^{\text{offspring}}\) to \(M_{new}\)
12:endfor
13:for each movement \(M_{k}\) in the retained movements do
14: Generate random perturbation \(\Delta M\)
15: Mutate movement: \[M_{k}^{\text{mutated}}=M_{k}+\Delta M\]
16: Add \(M_{k}^{\text{mutated}}\) to \(M_{new}\)
17:endfor
18:return\(M_{new}\)
```  
**Algorithm 4**Selection, Mutation, and Crossover of Candidate Movements  
Parallel Selection and Mutation To optimize the selection, mutation, and crossover steps of the algorithm, we can use the parallel processing capabilities of CUDA. By implementing the selection mechanism in parallel, each thread can independently evaluate the MSE of candidate movements against the threshold \(T\). This approach allows for simultaneous processing of multiple movements, significantly reducing the time required for selection. Similarly, the mutation process can be parallelized, where each thread generates a random perturbation \(\Delta M\) and applies it to a different retained movement. This ensures that multiple mutations occur simultaneously, further accelerating the overall computation. Furthermore, the crossover operation can also benefit from parallelization; pairs of movements can be selected and averaged in parallel across different threads, producing offspring movements rapidly. Using CUDA, we enhance the efficiency of these critical steps in the algorithm, allowing the exploration of a larger search space and facilitating quicker iterations within the optimization process.
## 4 Case Study and Experimental Implementation
### Dataset Description  
To investigate the various potential classes of failure in dynamic systems, two distinct datasets were employed: a factory dataset and an automotive dataset. The factory dataset comprises text descriptions of factory events collated from factory reports. Each event was described in brief in natural language and assigned a category indicating the type of failure. In total, three distinct categories of failures were identified: software failure, hardware failure, and communication failure [46]. The dataset comprises 480 descriptions of disparate events, each of which has been assigned one of three categories of cause. In contrast, the second dataset employed the Autonomous Vehicle Disengagement Reports, which compiled descriptions of failures that occurred during the testing of self-driving vehicles in California between 2018 and 2019 [47]. In particular, the dataset describes various failures in autonomous vehicle technology. However, the descriptions of the detected failures are presented in natural language without identifying the causes of each event, rendering the data unlabeled. The total number of events described is 455. Both sets of data are saved as CSV files, which are then used as input for the preprocessing phase  
Figure 5: System architecture of the case study with different test phases of V-Model.  
Figure 6: Photography of the virtual test drive platform with HIL simulation system.  
prior to training. Consequently, two CSV files comprising 935 samples are employed for the training and validation of the target model.  
In accordance with the proposed framework, the data undergoes a series of preprocessing steps, including the removal of special characters and digits, tokenization, i.e., the conversion of text into individual words, normalization, and the elimination of stop words and stemming. In order to facilitate the tokenization process, the text is transformed into a sequence of components, referred to as \"tokens,\" through the utilization of the Keras Tokenizer [48]. Consequently, the dataset is transformed into a binary matrix. The Snowball Stemmer [49] is a function that reduces words to their fundamental form through a process known as stemming.  
Following the completion of the preprocessing phase, the data are split with 80% allocated for training, 10% for validation, and 10% for testing. Following the cleaning steps, 748 samples were utilized for the training phase as unsupervised learning, while 93 samples were employed for the validation phase. The same number of validation samples were used as unseen data for the testing process.
## Appendix A Details for Training Via Soft Actor-Critic  
In this part, we elaborate on the settings of distributed SAC as our DRL algorithm. The network architecture of actor and critic is firstly shown. Figure 2 has already demonstrated an overall network design, and the detailed structure of convolutional blocks is illustrated in Figure 11. We also perform zero-padding for all previous rewards and actions \(O_{ar}\) when the current time horizon is smaller than the defined historical length, i.e., four in our case. For the visual observation \(O_{v}\), we perform replicate paddings of the RGB image at \(t=0\). As described in Section 3.3, we accelerate the training speed and improve the generalizability of the agent by paralleling nine agents, resulting in a distributed version of SAC. We show the pseudo code in Algorithms A1 and A2 for both worker process and master process. The asynchronous method for experience gathering is analogous to asynchronous advantage actor-critic [25]. Each worker process gets a copy of the shared actor from the main process and collects experience asynchronously. After one worker has completed an episode, the gathered experience is stored in a shared episode replay queue, and a new version of the shared policy is obtained from the master process. Concurrently, the master process updates the actor and critic networks and gathers all workers' experience from the shared episode replay queue to fill a PER buffer. The hyper-parameter settings are demonstrated in Table 11.  
```
input :\(\phi,\overline{\theta},f_{\pi},\mathcal{E}_{\mathit{S}},\mathit{env}\); \(\triangleright\) Shared parameters for the policy, the Q-Function and the Nav4 CL-Q network, shared episode replay queue, and a target environment for interaction input :L; \(\triangleright\) A task database consisting of initial states based on env
1 whileTruedo
2\(\mathcal{E}\leftarrow\emptyset\); \(\triangleright\) Initialize an empty local episode replay buffer
3\(\overline{\phi}\leftarrow\phi\); \(\triangleright\) Create a local policy network copy \(\overline{\phi}\) for the next episode
4\(L_{r}\leftarrow\) randomly sample 100 tasks from \(L\;\mu_{f},\sigma_{f}\gets FitNormal(f_{\pi}^{*}(H_{r}))\)\(\mathit{task}=\mathit{GetDynamikTask}-Q(\overline{\theta},\mu_{f},\sigma_{f},H)\); \(\triangleright\) Use Nav-ACL-Q to get a task that fits the current ability of \(\phi\) whilemaximal episodic length not reacheddo
5\(a_{t}\sim\pi_{\overline{\phi}}(a_{t}|s_{t})\); \(\triangleright\) Sample an action according to the local policy
6\(s_{t+1}\sim p(s_{t+1}|s_{t},a_{t})\); \(\triangleright\) Sample transition from the environment
7\(\mathcal{E}\leftarrow\mathcal{E}\cup\left\{(s_{t},a_{t},r_{t},s_{t+1})\right\}\); \(\triangleright\) Store the transition in the local episode replay buffer
8 endwhile
9\(\mathcal{E}_{\mathit{S}}\leftarrow\mathcal{E}_{\mathit{S}}\cup\mathcal{E}\); \(\triangleright\) Append the locally recorded episode to the shared episode replay queue
10 endwhile
```  
**Algorithm 1**Distributed Soft Actor-Critic-Worker Process.