esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Truncated Backbone Network
For the remaining residual blocks, the output can be acquired in the same  
Figure 4: The architecture overview of the truncated backbone network. computational process to form the cascaded result of the whole maintain path. Similarly, the second path is designed by combining a \(1\times 1\) convolution layer and a BatchNorm layer with a 1-step stride and zero padding. As we directly splice the outputs of the maintain path and the second path by going through matrix stitching operation, we can obtain a middle output of 256 channels. The remaining three CSP stages and the inter CSP stage has an obvious difference: by first passing through a down-sampling layer with \(3\times 3\) convolutions combined with a BatchNorm layer, we can obtain the middle output for other CSP stage 1-3, and the subsequent parts are the same as the inter CSP stage. Moreover, there is an additional interlayer consisted of \(1\times 1\) convolutional layer and BatchNorm layer between two adjacent CSP stages. The calculation processing of each CSP stage is shown as following:  
\[ \begin{array}{l}x_{a}=f_{3\times 3}(x)\\ g(x_{b})_{0}=x_{b}=f_{{}_{1\times 1}}^{b}(x_{a})\\ g(x_{b})_{i}=f_{\text{Res}(X)}(g(x_{b})_{i-1})\\ x_{c}=f_{{}_{1\times 1}}^{c}(x_{a})\\ f=(x_{c}\oplus g(x_{b})_{n})\end{array}  \tag{1}\]  
where \(x\) represents the input vector of each CSP stage. \(f_{3\times 3}()\) is the structure parameter of \(3\times 3\) down-sampling layer combined with BatchNorm layer, and the \(x_{a}\) is the corresponding output. Similarly, \(f_{1\times 1}^{b}(),f_{1\times 1}^{c}()\) are signifying the \(1\times 1\) convolutional operation going through two parallel branches severally. Then, \(g(x_{b})_{0}\) means the input vector of the 0-th residual block, which is equal to \(x_{b}\). \(g(x_{b})_{i-1}\) means the input vector of the \(i\)-th residual block, which will pass through multiple branches to calculate the output \(g(x_{b})_{i}\). \(f_{\text{Res}(X)}()\) represents the structure parameter of the partial residual operation. \(x_{c}\) means the output vector of the relatively tiny path. \((A\oplus B)\) indicates the splicing operation between \(A\) and \(B\) in the channel dimension. The variable \(n\) represents the number of residual blocks in different CSP stages, which is set as \(n=\{3,3,5,2\}\) for four stages. Finally, the global average pooling (GAP) is applied to establish the output layer of the whole truncated backbone, which exports the multi-dimensional feature maps \(F\) with the channel number up to 2048. The expression is as follows:  
\[F=H_{csp}(\widetilde{x},\widetilde{y}) \tag{2}\]  
where \(H_{csp}()\) denotes the structure parameter of all cascaded layers. \((\widetilde{x},\widetilde{y})\) is the input pest images and estimation labels. During the training and verifying process, the activation function is defined as Mish function, of which function expression is showed as:  
\[f(x)=x\cdot\tanh(\ln(1+\varepsilon^{x})) \tag{3}\]
## 5 Discussion
### Updated Forest Aboveground Biomass  
It was shown in Figure 5 that the forest AGB was mainly distributed in the southern part of Genhe County and northern part of Yake County. A more detailed spatial distribution pattern can be found in the ALS coverage area due to its high spatial resolution. When comparing the BEPS- with ALS-based AGB in 2012 (Figure 6), although they showed a similar distribution pattern, ALS-based estimates were much lower compared with the corresponding low value regions in the BEPS-based results. This was because only the trees whose heights were larger than 2 m were used from the ALS data to compute forest AGB and led to ALS data being unable to capture low AGB value regions. For the high value regions, the ALS-based AGB were slightly higher than BEPS-based results. This might be attributed to the saturation problem for the optical remotely sensed images when used for LAI inversion. Another reason may result from the different accuracy and spatial resolution of land cover maps obtained from Landsat data and ALS data. The Landsat-based land cover was produced using the supervised classification by manually selected training samples, while the ALS-based land cover  
Figure 8: The comparisons between the reflectance values of the PIFs objects for band 1 (**a**), band 2 (**b**), band 3 (**c**), band 4 (**d**), band 5 (**e**) and band 6 (**f**) of Landsat ETM+ images in 2008 and 2012.  
was produced using the category information delivered by the vendor. In fact, the validated spatially continuous ALS-based AGB map can serve as a \"bridge\" for validation and comparison between the AGB results obtained from coarse remotely sensed data and field-based results at the forest plot level.  
The BEPS-based annual mean forest AGB was 7.13 kg/m\({}^{2}\) for the period from 2008 to 2012, which was similar to but slightly larger than the result (i.e., 6.25 kg/m\({}^{2}\)) obtained in the same forest region by another research [59]. This might be due to the older measurements used in their work (year 2003). During the period between two field-based data, forest AGB would increase due to the strict forest protection policy in our study area. In addition, [PERSON] et al. [60] found that most Landsat TM-based forest AGB values were close to level of 6-10 kg/m\({}^{2}\) in the northeastern part of China after 2000. Meantime, by combing optical and SAR data, [PERSON] et al. [61] found that the biomass values were mainly distributed over the range from 7 to 12 kg/m\({}^{2}\) in Genhe, China for 2013. Their results were similar to and further testified the results in this research.
# Molecular Subtyping and Outlier Detection in Human Disease Using the Paralique Algorithm
## 3 Experimental Results
### A Search for Unrecognized Subtypes
#### 3.2.3 Chronic Lymphocytic Leukemia  
Chronic lymphocytic leukemia is one of the most common types of leukemia, with pathogenesis characterized by an overproduction of neoplastic B cells in the bloodstream. The current median age at diagnosis is 65, with males affected more often than  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **GO Category** & \(\boldsymbol{p}\)**-Value** \\ \hline Asthma GSE4302 & Oxoreductase & \(1.1\times 10^{-4}\) \\ Breast Cancer GSE10810 & Secreted & \(1.0\times 10^{-13}\) \\ Chronic Lymphocytic Leukemia GSE8835 & Mhc ii & \(2.4\times 10^{-15}\) \\ Colorectal Cancer GSE9348 & Translational elongation & \(2.8\times 10^{-28}\) \\ Lung Cancer GSE7670 & Secreted & \(7.7\times 10^{-10}\) \\ Multiple Sclerosis GDS3920 & Translational elongation & \(1.9\times 10^{-34}\) \\ Pancreatic Cancer CDS4102 & Signal & \(4.59\times 10^{-15}\) \\ Prostate Cancer GSE6919 & Translational elongation & \(4.92\times 10^{-46}\) \\ Psoriasis GSE13355 & Immune response & \(3.5\times 10^{-15}\) \\ Schizophrenia GSE17612 & Organelle membrane & \(5.24\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: GO enrichments. Listed is the GO term category with the lowest enrichment \(p\)-value of the 100 most differentially expressed genes for each disease in this study.  
females [52]. Chronic lymphocytic leukemia typically presents with a slow progression in which patients are able to enjoy a more or less normal life expectancy. In some cases, however, chronic lymphocytic leukemia can be aggressive, with death occurring less than five years after the onset of symptoms.  
GEO series GSE8835 data were instead derived from the Affymetrix Human Genome U133A Array with 22,283 probes, and were designed to study the effects of chronic lymphocytic leukemia on T cells in peripheral blood [53]. The study comprised 24 CD4 cell samples from chronic lymphocytic leukemia patients and 12 CD4 cell samples from healthy, age-matched donors.  
Filtering reduced the number of probes to 1338. At a threshold of 0.8, our tools produced two paralogues of size 4 and 18, leaving two samples unclassified. The most differentially expressed genes across these two putative subtypes included ZAP-70, previously identified as the best discriminator of Ig-mutated and Ig-unmutated chronic lymphocytic leukemia [54].
## 3 Results and Discussion
### Electrochemical Assessment
#### 3.2.3 EIS Analyses
\(\eta_{\mathrm{pass}}\) was around 0.9 for all conditions, meaning a smooth passive film. n can be inversely proportional to the surface roughness of the element [26]. R\({}_{\mathrm{pass}}\) had dissimilar evolution over the time according to the type of sample. For the wrought sheet sample, R\({}_{\mathrm{pass}}\) decreased over time whilst R\({}_{\mathrm{pass}}\) increased with time for the LPBF sample. The larger size of the grain encourages the improvement of the passive film corrosion resistance over time [7; 8]. For the bared material, CPE\({}_{\mathrm{bared}}\) fluctuated over time for both samples. \(\eta_{\mathrm{bared}}\) diminished with time for both samples to values close to 0.5. The dielectric relaxation processes are indicated by this value [27]. R\({}_{\mathrm{bared}}\) of the wrought sheet sample increased over time. The internal oxidation of the bared material can increase the corrosion resistance of the bared material, meaning an internal corrosion. The LPBF sample R\({}_{\mathrm{bared}}\) fluctuated with the time. Note that resistance values were similar to \(R_{p}\) from PPC (Table 2). Figure 6: Equivalent circuit for representing the corrosion mechanism evolution over time for the samples in 0.6 M NaCl.
# A Vector Sensor-Based Acoustic Characterization System for Marine Renewable Energy
## Materials and Methods
### Hardware
This frequency range is particularly suited to measurement of sounds from MRE installations that primarily emit sound below 500 Hz [11] and marine mammals whose hearing sensitivities fall within this frequency range (50 Hz-3 kHz). Examples of marine mammals that primarily vocalize in (and are, therefore, sensitive to) this frequency range are the larger cetaceans that include mysticete whales and sperm whales (specific species include the blue whale, fin whale, gray whale, Bryde's whale, right whales, minke whales, bowhead whales, humpback whales, sei whales, and beaked whales) [14]. The nominal pressure sensitivity of the pressure channel at 1 kHz is \(-\)179 dB re V/\(\upmu\)Pa, which, along with the saturation level of the sensors, allows for measurement of sound pressure levels up to 240 dB re 1 \(\upmu\)Pa at a distance of 100 m from the sensor. This level is well above sound pressure levels that can cause permanent threshold shifts in marine mammal hearing [3]. A custom low-noise, low-power data logger was designed to synchronously sample multiple analog and digital compass channels. Twelve analog channels (x, y, z, and pressure for three sensors) of time-synchronized VSA data at 24 bits and one digital channel (orientation) are recorded by the custom data logger at a synchronous sampling rate of 20 kHz per channel. Analog sensor output is converted to a digital format, and all data are stored on a solid-state drive. The data logger, in a stainless steel pressure housing, is mounted on the bottom fiberglass grate. The NoiseSpotter is designed to operate autonomously for up to three weeks without servicing. In-water tests indicate that the hardware configuration is suitable for small-vessel (8 m) operations in quiescent, energetic, and deep-water environments.
# Hybrid RF/VLC Network Architecture for the Internet of Things
## 4 System Design  
In this section, the designed architecture, the frame formats, and the segmentation and routing protocols are presented. In the first subsection, the developed protocol-abstraction layer and its frame format are described. This layer allows for the routing and identification of all nodes independently of their sub-network.
## 2 Materials and Methods
### Analogue Front-End  
The analogue front-end block diagram is shown in Figure 2. It comprises a charge-sensitive preamplifier with pole-zero cancellation, a three-stage shaper, and a single-ended to differential converter. A fast peaking time of the shaper \(T_{peak}\approx 25\) ns is required. Moreover, the signal must return to baseline quickly to minimise the pile-up and spill-over in the next bunch crossing, thus allowing clear separation between subsequent pulses at the LHC bunch crossing rate of 40 MHz. In addition, a very low power consumption 1-2 mW/channel is required. The front-end must function well with a sensor capacitance between 1.6-12 pF and input signals of both polarities, providing a signal-to-noise ratio above ten (S/N \(>\)10). The last block in the analogue chain converts a single-ended shaper output to a differential signal which is then sent to the ADC.  
One of the biggest challenges for this block was to obtain a very short tail of the shaper output pulse (about 5% amplitude after 2\(T_{peak}\)) with minimal power consumption. Preliminary studies showed that this goal is not achievable with a standard semi-Gaussian shaping using a reasonable number of real poles in the transfer function. Therefore, a more advanced three-stage shaping, based on complex poles and zeros in the transfer function [17] was implemented. The first stage has a real pole; the second, in the multiple negative feedback configuration, has two complex poles, while the third one, in Boctor configuration [18], has two complex poles and two complex zeros. The shaper transfer function was optimised in simulations to get the most symmetrical shape of the output pulse.  
All shaper stages are referred to the same common voltage value (\(vcm\_sh\)), although generated separately for each of them (\(vcm\_sh1\), \(vcm\_sh2\), \(vcm\_sh3\)). The same common voltage value is also generated in the single-ended to differential converter (\(vcm\_s2d\)). A common 6-bit DAC is used for the common voltage setting. Moreover, the bias currents of all front-end blocks (preamplifier, shaper, single-to-differential converter) can be adjusted using a dedicated internal 5-bit DACs. Additionally, every channel contains an 8-bit trim DAC for precise baseline setting. To mimic the pulses from a silicon sensor, each channel is equipped with a small (100 fF) test capacitance connected to an internal pulse generator. The amplitude of the pulse is controllable via another dedicated 6-bit DAC. This is an important feature in the testing the response of the SALT over the full dynamic range.
## 3 Approach
### Transmission of Target Value and FrequencyTable, Synchronization of Transaction Pool
```
1:procedureTransactionPool Synchronization
2:\(\text{Device}_{\text{sensor}}\)executes:
3:\(\text{TargetValue}_{\text{sensor}}\) = SHA256(list)
4:\(\text{Send}\ \text{Encrypt}(\text{DeviceKey}_{n-1},\text{TargetValue}_{\text{sensor}}+\text{FrequencyTable}_{\text{sensor}})\) to \(\text{Device}_{\text{gateway}}\)
5:\(\text{Call}\ \text{Module}\ \text{DeviceKey}Generation()\)
6:\(\text{Device}_{\text{gateway}}\)executes:
7:\(\text{TargetValue}_{\text{gateway}}\) = SHA256(list)
8:\(\text{Receive}\ \text{Encrypt}\ (\text{DeviceKey}_{n-1},\text{TargetValue}_{\text{sensor}}+\text{FrequencyTable}_{\text{sensor}})\)
9:\(\text{Device}_{\text{sensor}}\)
10:\(\text{Decrypt}(\text{DeviceKey}_{n-1},\text{TargetValue}_{\text{sensor}}+\text{FrequencyTable}_{\text{sensor}})\)
12:ifloss data then:
13:\(\text{lossFrequencyList}\ =\text{FrequencyTable}_{\text{sensor}}\)-\(\text{FrequencyTable}_{\text{gateway}}\)
14:\(\text{PermutationList}\ =\text{Permutation}(\text{lossFrequencyList})\)
17:whilePermutationList is not empty:
18:\(\text{candidate}\text{Nonce}\ =\text{PermutationList}.\text{pop}()\)
19:if(\(\text{TargetValue}_{\text{sensor}}==\text{FindNonce}(\text{ transactionPool},\text{candidateNonce})\):
20:\(\text{Syncronization}(\text{transactionPool},\text{candidateNonce})\)
21:\(\text{Call}\ \text{Module}\ \text{DeviceKey}Generation()\)
22:else:
23:if(\(\text{TargetValue}_{\text{sensor}}==\text{TargetValue}_{\text{gateway}}\)):
24:\(\text{Call}\ \text{Module}\ \text{DeviceKey}Generation()\)
25:endprocedure
```  
**Algorithm 3**TransactionPool Synchronization  
Figure 9: TransactionPool Synchronization.
# An Intelligent Method for C++ Test Case Synthesis Based on a Q-Learning Agent
## 2 Materials and Methods
### The Agent Training  
At the initial stage, pretraining takes place. The pretraining is simulated based on the function call sequences extracted from the original test cases. The agent does not select actions independently but instead replicates the actions that were observed in original TC, receiving rewards for reproducing all steps sequentially. The pseudocode of pretraining Algorithm 1 is shown in below. ```
1:Input:\(TC_{0}\) Original TC
2:\(k>0\) Call history depth
3:\(\alpha\in(0,1]\) Learning rate
4:\(\gamma\in(0,1)\) Discount factor
5:eps
6:Output:\(Q\) Pretrained Q-Table
7:Body:
1:\(Q\leftarrow\oslash\)
2:for all \(eps\)do
3: local \(TC\leftarrow\oslash\)
4: local \(\mathcal{A}_{ep}\leftarrow\text{GetActionSpace}(TC_{0})\)
5:for each \(a_{t}\in\mathcal{A}_{ep}\)do
6: local \(s_{t}\gets Suffix_{k}(TC)\)
7:\(TC\gets TC\#a_{t}\)
8: local \(s_{t+1}\gets Suffix_{k}(TC)\)
9: local \(r_{t}\leftarrow\text{Execute}(TC)\)
10:\(Q\gets Update(Q,\ r_{t},\ a_{t},s_{t+1},\ \alpha,\ \gamma)\)
11:endfor
12:endfor
13:return\(Q\)
```  
**Algorithm 1** Agent Pretraining Algorithm  
Next, we present the generalized agent training Algorithm 2. The Q-table is updated during the process of constructing a TC by the agent. At each step, the agent is in a specific state \(s_{t}\), selects an action \(a_{t}\), appends it to the TC, and the resulting TC is then executed in a virtual environment. Execution results are evaluated using the reward \(r_{t}\), and the Q-table is updated according to Equation (1). Unlike classical episodic updates performed after complete execution, in our implementation, the Q-table is updated after each appended action of the TC, enabling faster adaptation of the policy to new action combinations. Rollback is used to handle invalid action sequences during test case construction. The agent may roll back and retry action selection up to a predefined number of steps. The central component of the agent's learning process is the reward function, which defines which actions are considered beneficial. In the following, we describe its structure and the logic behind assigning positive and negative feedback.
## 4 Discussion
### Justification of the Results
#### 4.2.2 Different Speaker Placement Method  
For different speaker placements, results (Figure 11, Table 3) indicate that the best method for measuring RT is the method of 12 positions. This method has the least mean absolute error and standard deviation compared with measurements with the dodecahedron speaker. Therefore, this method is to be preferred for RT measurements.  
A surprising aspect of the data (Figure 11, Table 3) was that for C\({}_{80}\) and D\({}_{50}\) measurements, the methods of 26 and 14 positions have smaller mean absolute error in the high frequency range than the method of 12 positions. Those results were obtained with a two-way speaker ([PERSON], Willich, Germany) with a tweeter for the high frequency range It can thus be conceivably hypothesized that because of the higher directivity of the tweeter speaker for the high frequency range, higher number of impulses is necessary in order the emitted summed sound field to resemble the one emitted from the dodecahedron speaker. This rather contradictory result may have possible implications for the improvement of the method that are going to be discussed in the future research section (design of a speaker for high accuracy measurements and auralization).
## 3 Tests, Results, and Discussion  
In order to evaluate the performance of the proposed model, a set of land-borne data was processed and analyzed. This first subsection demonstrates the experiment details, including the experimental equipment, satellite availability, and data processing schemes. The second subsection assesses the accuracy of the PPP-B2b corrections and the positioning performance of BSSD PPP. The third subsection validates the impacts of INS, the odometer on PPP positioning, and the effect of the BSSD model on the positioning performance of BSSD PPP/INS/ODO TCI. The last subsection analyzes the influences of the odometer and BSSD model on attitude determination.
## 2 The Proposed Methodology
### U-Net Based Semantic Segmentation  
U-Net was introduced in biomedical imaging to improve precision and localization of microscopic images of neuronal structures. The architecture builds upon the fully convolutional network [15] and is similar to the deconvolutional network [16]. In a deconvolutional network, a stack of convolutional  
Figure 3: Examples of eardrum conditions observed in our dataset. The representative frame for each category was selected by a trained researcher: (**a**) otitis media with effusion, (**b**) tympanosclerosis, (**c**) eardrum perforation, (**d**) retraction, (**e**) acute otitis media (AOM), and (**f**) cholesteatoma.  
Figure 2: Examples of annotation. (**a**) Original tympanic otoscopy images; (**b**) annotated images. Glare and blur are excluded from the target area by the annotator.  
layers--where each layer halves the size of the image but doubles the number of channels--encodes the image into a small and deep representation. That encoding is then decoded to the original size of the image by a stack of up-sampling layers. The U-Net adds additional skip connections between layers at the same hierarchical level in the encoder and decoder. This allows low-level information to flow directly from the high-resolution input to the high-resolution output.  
We trained a U-Net architecture with an encoder depth of four to automatically find eardrum regions in each otoscope video frame. In order to reduce the effects of overfitting, we took advantage of data augmentation [17], which involves random horizontal flips, image sharpener, affine transformations between \(-45\) and \(45\) degrees, and elastic transformations with three different \(\alpha\) intervals of (45, 50), (55, 60), and (65, 80) with \(\sigma=5\) as described in [18]. The augmentation process was implemented using the _imgaug_ library [19]. Then, to scale inside the image randomly, images were shrunk and enlarged within a range of (\(-0.5\), \(+0.5\)). After data augmentation, we had 15,280 additional images, i.e., generated 20 augmented images for each of the 764 images in Dataset 1, to train the segmentation network.  
The performance of the segmentation algorithm was computed in a \(k\)-fold cross-validation [20] (with \(k=10\)). It should be emphasized that the segmentation framework was exposed neither to the validation images (Dataset 2) nor the augmented images resulting from the validation set.