esa-sceva/satcom-chunk-collection huggingface dataset:
# LED Current Balance Using a Variable Voltage Regulator with Low Dropout \(v_{DS}\) Control
## 2 Aspect of Linear Current Regulator for LEDs
### LED String Module in LCR
A thick-green dc load line of the MOSFET on the \(i_{DS}\)-\(v_{DS}\) plane is presented by (11), and the corresponding string \(N\)-LED characteristic curve on the \(i_{D}\)-\(v_{D}\) plane is given by (2), as respectively shown in Figure 3a. In other words, the operation point of MOSFET is at (\(I_{DS20}\), \(V_{DS20}\)) and that of the \(N\)-LEDs is at (\(I_{D20}\), \(V_{D0}\)), where \(I_{DS20}=I_{D20}\), while we presume that the point \(q_{20}\) has a low dropout voltage \(V_{DS20}\). For the load line on the \(i_{DS}\)-\(v_{DS}\) plane in this situation, the supply to the drain of the MOSFET is equivalent to \(V_{M2}\) under the gate drive of \(v_{GS2}\) in (11) and the \(V_{DS2}=(V_{M2}-V_{Rs})\). Temperature falling to \(T=T_{-}\)  
Once the environmental temperature \(T\) falls to \(T_{-}\) from \(T_{0}\), where \(T_{-}<T_{0}\), the string \(N\)-LED voltage will increase to \(V_{D2}=Nv_{D-}\), due to the negative temperature-dependent coefficient, and results in the \(V_{M2-}\) decreasing to \(V_{dc}-NV_{D-}\). In this situation, the green-thick dc load line will move to the left, as a red line, leading the operation point \(q_{20}\) moving left to \(q_{2-}\) while \(v_{DS}=V_{DS2-}\), and while the MOSFET operates in the triode region and its drain current is \(i_{DS}=I_{2-}\); correspondingly, when the string \(N\)-LED current decreases, reducing its luminance, even its voltage \(V_{D}\) is slightly increasing. Accordingly, it may happen to push the operation point from the saturation region into the triode region, due to the increase of \(N\)-LED's voltage in a lower temperature situation. Ambient Temperature rising to \(T=T_{+}\)  
If the temperature increases to \(T_{+}\) from \(T_{0}\), where \(T_{+}>T_{0}\), the green-thick dc load line will move right, as a blue line, due to \(V_{M2+}\) increasing to \(V_{dc}-NV_{D+}\), which leads the operation point \(q_{20}\) to move right to \(q_{2+}\) on the flat \(v_{GS2-}\) characteristic curve. Accordingly, the \(i_{DS}=I_{2+}\approx I_{20}\) and even the \(v_{DS}\) increases to \(V_{DS2+}\); meanwhile, the string \(N\)-LED current is \(i_{D}=I_{2+}\approx I_{20}\), which keeps its luminance at almost the same value as that produced at \(I_{2}\). Bias Situation for the MOSFET with Constant \(V_{dc}\) Supply for \(T_{+}>T_{0}>T_{-}\)  
In order to explain the drain-to-source voltage of the MOSFET for \(T_{+}>T_{0}>T_{-}\) under a constant voltage \(V_{dc}\) supply, a voltage sum \(v=Nv_{D}+i_{DS}R_{s}\) is introduced in Figure 3b. The voltage \(v\), as the dark brown line, clearly displays the variation of \(v_{DS}\) between saturation and the triode regions, due to the temperature effect, where \(v_{DS}=V_{dc}-v\).
## 3 Results
### Structural Repair and Airframe Condition Monitoring by Using RFID and CMB
#### 3.5.1 Contact Memory Button (CMB)  
One of the smart ways of storing data related to specific components represent the contact memory button or CMB, which was widely used in military application, but now it may find its application in the civil sector as well. This device is a form of automated identification technologies (AIT) capable of storing data in various forms and formats such as a small USB flash disk, including images, PDF copies of repair orders, and re-inspection requests. The capacity of these devices is up to 4 gigabytes of data. These data could be represented as the part number or serial number, date of its manufacture, or its maintenance history. The data saved on RFID tags can be read or be updated wirelessly by a hand scanner [43]. By integrating RFID, we can improve airframe maintenance by allowing mechanics to use a CMB (Contact Memory Button) tag, which records all relevant data on previous and current repairs made to the airframe design. This solution allows placement and easy display in case of subsequent maintenance. Such use has advantages especially when used on composite structures and allows detecting hidden repairs--repairs that were not recorded or uploaded to the contact memory button (CMB). However, this device is not an ideal choice in terms of increasing the effectiveness of visual inspection performed by UAV, which is mainly due to the reason that these buttons could not be read wirelessly from distance, as they work only with the USB interface. Therefore, this option is possible only when the visual check is performed by a mechanic.
# Temporal Convolutional Network-Based Axle Load Estimation from pavement Vibration Data
## 4 Results and Discussion
### Reconstruction Results for the Load--Time History  
The TCN load--time-history reconstruction network was constructed according to the structure presented in Figure 3, and the training strategy is as illustrated above. For Site 1, the length of the input sequences was 5 \(\times\) 700, which corresponds to a signal duration of 1.4 s, using half of the sensing loop data. The TCN network reconstructed the time histories of 1338 test samples, whose speed and load were unknown in advance, using a batch size of 32. For Site 2, the length of the input sequences was 8 \(\times\) 700, corresponding to one row of the sensing loop data. The TCN network reconstructed the time histories of 201 test samples whose vehicle locations were unknown in advance, using a batch size of 16.  
To illustrate the results of the TCN identification method, the reconstructed impact loads of three samples from Site 1 and four samples from Site 2 are presented in Figures 7 and 8. The main impulse can be accurately reconstructed; however, some errors occur in the reconstruction of the peak so the capability of the model to capture high-frequency fluctuation features needs improvement in the future.  
To quantitatively evaluate the performance of TCN, the peak error (PE), main impulse error (MIE), and root mean square error (RMSE) are calculated and compared in Figure 9. The results indicate that the proposed method achieves good performance for both Site 1  
Figure 6: The history of the vehicle load and the vibration signal measured from the sensing loop under vehicles’ wheels. (**a**) Passenger car; (**b**) Lorry; (**c**) Truck-I; (**d**) Truck-II.  
and Site 2. According to Equation (1), for both Site 1 and Site 2, all samples result in 11.5% PE, which is close to the errors allowed by the Long-Term Pavement Performance Program (LTPP) of 10% [1]. Compared to Site 1, the peak error is slightly increased and the RMSE is well behaved for Site 2. Since Site 2 is a practical scenario, it can be concluded that the proposed TCN model has good time-history reconstruction accuracy for axle loads under conditions of complex traffic.  
The results indicate that the temporal convolutional network (TCN) model exhibits excellent load inversion accuracy for both sizes of cement concrete pavement. For Site 2, the TCN model demonstrated robust performance when subjected to natural traffic flow with varying speeds and vehicle types. Hence, it can be concluded that the TCN model performs well under different sizes of cement concrete pavement and varied load conditions. However, the current study lacks experimental design for asphalt pavement. In future research, we plan to expand the investigation to include asphalt roads to validate the model's performance in inverting loads from natural traffic flow on asphalt roads.  
Figure 7: Site 1 reconstruction results. (**a**) speed = 2 m/s, load level 1; (**b**) speed = 2 m/s, load level 5; (**c**) speed = 4 m/s, load level 5; (**d**) speed = 6 m/s, load level 5.
# Compressive Sensing-Based IoT Applications: A Review
## 4 CS-Based IoT Applications
### Sensing Layer
#### 4.1.2 Weighted Measurements  
Another approach to optimizing the number of transmitted samples is to assess a weight to each sensor depending to its importance in the application (a scenario that often occurs in the heterogeneous WSNs). This idea was first introduced in the context of CS for image reconstruction using wavelet decomposition where different CFs are assigned to different wavelet sub-bands [79]. Subsequently, borrowing the same concept to IoT applications, [PERSON] et al. [80] introduced weighted CoSaMP (wCoSaMP) reconstruction algorithm to enable a new framework for data acquisition in heterogeneous IoT platforms where different type of sensors have different impact on the overall system, for instance, for IoT-based connected health, the ECG data are more crucial than humidity and temperature data. Applying this concept, fewer measurements are needed to achieve the same performance as conventional CS.
## 4 Results and Discussion
### Loop 2 Transport Emissions  
The Loop 2 \"Transport emissions\" (Figure 3) outlines a negative feedback mechanism aimed at controlling emissions from traditional transportation (Balancing Loop).  
If the Portion of alternative vehicles is low, the use of traditional vehicles increases, leading to a rise in the number of shipments conducted by conventional means. This results in a greater number of kilometers traveled by such vehicles, causing an increase in fossil fuel consumption and, consequently, in transport emissions. The rise in emissions can negatively affect traditional transport costs (because emissions are internalized), making it less competitive compared to more sustainable solutions. Over time, this effect may encourage the gradual adoption of alternative vehicles, closing the balancing loop. It describes a self-regulating dynamic, where the initial prevalence of traditional vehicles  
Figure 3: Loop 2 Transport emissions (Balancing Loop).  
Figure 2: Loop 1 Energy Loop (Balancing Loop).  
triggers economic and environmental effects that, in the medium to long term, push the logistics system toward rebalancing through the integration of more sustainable technologies. It is important to note that the overall level of fossil fuel consumption also depends on the efficiency of the vehicles used, while the capacity of traditional vehicles influences the total number of shipments required. These elements significantly impact the environmental footprint of the system and should be considered when analyzing urban access policies and sustainable logistics strategies.
## 3 Materials and Methods
### Decoder  
The encoder outputs high-level semantic feature maps, and the resulting deep and shallow semantic features are passed to the decoder [33]. However, since the VIT network is a powerful feature extractor and does not include a corresponding decoder for performing the semantic segmentation task, we use the decoder designed by [PERSON] et al. [34] specifically for Transformers as the decoder for the VIT network. The decoder therefore unfolds as four \"bridge\" stages that successively upsample the ViT token maps from 1/32 to 1/4 resolution. Each stage first applies a 1 \(\times\) 1 projection to match channel width, injects local context through a depth-wise 3 \(\times\) 3 convolution (a lightweight substitute for windowed self-attention), and then blends the result with the lateral encoder feature via a sigmoid-controlled skip-fusion gate. After the final fusion, a SegFormer-style MLP head (depth-wise conv \(\rightarrow\) GELU \(\rightarrow\) 1 \(\times\) 1 conv) produces the full-resolution logits. Although the average F1 gain over a standard U-Net decoder is only +3.59 percentage points, statistical testing shows the improvement is significant, and the decoder delivers a much larger boost to the minority cotton class while remaining lighter and faster to train than its CNN counterpart. For these reasons we retain this Transformer-adapted decoder in the final network. The core idea of this decoder is to use upsampling to recover spatial resolution, as shown in the following equation (see Formula (13)).  
\[X_{\text{upsampled}}=\text{Upsample}(X_{\text{encoded}},s) \tag{13}\]  
where \(X_{\text{encoded}}\) is the low-resolution feature map obtained from the encoder, and \(s\) is the upsampling factor. The skip connections combine low-level detail features with high-level semantic features as follows: \(X_{\text{skip}}=\text{Concat}\Big{(}X_{\text{encoded}},X_{\text{upsampled}} \Big{)}\), while the Transformer processes global dependencies. Here, \(X_{\text{encoded}}\) is the feature map output by the encoder, and \(X_{\text{upsampled}}\) is the upsampled feature map. Finally, the classification is performed using  
Figure 7: Principle diagram of the PAM.  
the Softmax function, generating pixel-level classification results with the same size as the input image (see Formula (14)).  
\[\hat{y}=\mathit{Softmax}\big{(}W_{output}X_{final}\big{)} \tag{14}\]  
where \(X_{\text{final}}\) is the final output that integrates all of the processed features, and \(W_{\text{output}}\) is the learnable weight matrix used for the output.
# Resolving Persistent Packet Collisions through Broadcast Feedback in Cellular V2X Communication +
## 2 Related Work
### Using Broadcast Feedback  
[PERSON] et al. [12] tried to solve the hidden node problem by using the broadcast feedback. The feedback is implemented as a bitmap that records successful receptions in the recent past in terms of the resource locations, which uses the padding space in each physical layer transport block (TB). Each vehicle collects and processes the feedback information to decide whether or not it should move to a different resource to avoid persistent packet collisions. A concerning aspect of this proposal, however, is the available padding space size that can arbitrarily change from one TB to another, and that can be small for monitoring the channel for more than several hundred milliseconds, shorter than the inter-packet gap, which can be as long as one second [7; 13]. Moreover, the positions of the feedback provider population are not considered in the work. Closer receivers tend to provide more accurate reports, which farther ones may contradict.  
[PERSON] et al. [14] proposed to use neighbor feedback to evade intentional and persistent packet collision attempts. When a packet collision occurs, a witnessing vehicle that happens to use the same subchannel next can provide a collision feedback upon which the victim reselects the resource immediately. This same idea may be employed to address unintentional packet collisions. However, the work has a limitation in that a single feedback provider is chosen so that if the only feedback provider is not in an opportune position, the feedback becomes less helpful. [PERSON] et al. [15] also proposed to use the broadcast feedback in the context of NR V2X to completely eliminate the continual frequency resource change in SPS, which is necessary to shield SPS from the peril of persistent packet collisions. It is not questionable, however, that the standard framework will accommodate the fully persistent scheduling.  
The proposed broadcast feedback scheme in this paper departs from these existing works in several aspects, as follow:  
* It resolves packet collisions in, at most, two collisions by executing the resource reselection, preventing further collisions;
* It can be implemented in the current standard framework. It can use the reserved bits in the SCI in LTE V2X or newly available resource reservation pointers in NR V2X;
* It works with little bandwidth overhead, so it is usable even under high channel utilization conditions;
* It does not rely on a particular feedback provider, so it is more reliable;
* Below, the proposed scheme is discussed in detail.
# Design Criteria for High-Gradient Radio-Frequency Linacs
## 3 RF Breakdown  
### RF Breakdown Properties  
A vacuum rf breakdown is an event that causes a measurable change in the RF power transmitted through and reflected from a resonator within nanoseconds. This breakdown triggers an increase in gas pressure, a bright flash of visible light, and a burst of X-rays.  
The properties of rf breakdowns vary between broadband structures, such as TW accelerating structures and high-power waveguides and narrow-band SW accelerating structures. In broadband structures, the power transmitted through the structure drops to almost immeasurable levels within 20-200 ns during a breakdown. Up to 80% of the incoming RF power can be lost in this process.  
Conversely, in SW structures most of the incoming RF power is reflected from the cavity [51]. However, it is worth noting that some multicell SW structures do not produce significant reflection during breakdowns.
# Effects of Sinusoidal Model on Non-Parallel Voice Conversion with Adversarial Learning
## 4 Modified Generative Adversarial Networks
### Network Architecture  
We use a 2D convolutional neural network (CNN) to build the generator and discriminator. The generator network comprises two convolutional layers, six CNN residual blocks [35], two transposed CNN layers, and a 2 \(\times\) 2 stride for downsampling and upsampling. In contrast, two separate five CNN layers are used for discriminator and classifier networks. Instance normalization [36] is used for the generator as this greatly improves the stability of the training but no normalization is employed for the discriminator. All these layers are followed by a rectified linear unit (ReLU) activation function, and the output layer followed by a sigmoid activation function. Each model is trained using _Adam_ as the optimizer [37] with \(\beta 1=0.9\) and \(\beta 2=0.999\). The batch size is set to 32.  
```
1:Feature extraction and initialization
2:\(x:=\) source features; \(c:=\) speaker label; \(y:=\) target features
3: Set parameters \(G\); \(D\)
4: Initialize batch size \(m\); learning rate \(\eta\); weights for losses \(\lambda\); number of total iterations \(n\)
5: Adversarial training model
6:for\(epoch=n_{1},\dots,n_{k}\)do
7:for training examples in \((x,c,y)\)do
8:for\(i=1,\dots,m\)do
9:\(\hat{y}=G(x,\;c)\)
10:\(D(\hat{y},c^{\prime})\)
11:endfor
12: optimize \(G\) and \(D\) by minimizing losses \(\mathcal{L}_{G}\) and \(\mathcal{L}_{D}\): \(\mathcal{L}_{G}\cdot\mathcal{L}_{adv}+\lambda_{cls}\mathcal{L}_{cls}^{fake}+ \lambda_{rec}\mathcal{L}_{rec}\) \(\mathcal{L}_{D}\cdot\mathcal{L}_{adv}+\lambda_{cls}\mathcal{L}_{cls}^{real}\) \(G^{*}\), \(D^{*}=\underset{G}{\text{argmin}}\mathcal{L}(G,D)\)
13: Update \(D\) while fixing \(G\)
14:endfor
15:endfor
16:endfor
17:endfor
18:endfor
19:
20: Generation of converted speech
21:for training data in \((x,c^{\prime},y)\)do
22: generate \(contF\), \(MVF\), \(MGC^{*}\)
23: synthesis CSM(\(contF\), \(MVF\), \(MGC^{*}\))
24:endfor
```  
**Algorithm 1** Acoustic modeling for voice conversion
## 2 The NC-RRT Method  
### The Method of Gradually Changing the Sampling Area  
We propose the method of gradually Changing the Sampling Area based on RRT (CSA-RRT) to guide exploration; the process is shown in Algorithm 3. As in [17], we need to calculate the distance (the Euclidean distance is used in this paper) between the goal configuration point \(q_{goal}\) and the configuration point that is initially the farthest from the goal \(q_{f}\) in the C-space, which is represented by \(D_{farthest}\) here, to ensure the completeness of the solution space. Assuming that the dimension of the C-space is \(s\), \(D_{farthest}\) is solved as follows in Equation (1):  
\[D_{farthest}=\sqrt{(q_{goal(1)}-q_{f(1)})^{2}+...+(q_{goal(s)}-q_{f(s)})^{2}}. \tag{1}\]  
```
1:\(T\leftarrow\text{InitTree}(q_{start})\);
2:\(R\gets D_{farthest}\);
3:for\(i=1\) to n do
4:\(q_{rand}\leftarrow\text{RandomSample}(i)\);
5:if\(\text{Distance}(q_{rand},q_{goal})>R\)then
6:continue;
7:endif
8:\(q_{near}\leftarrow\text{NearestNeighbor}(q_{rand},T)\);
9:\(q_{new}\leftarrow\text{Extend}(q_{rand},q_{near},\varepsilon)\);
10:if CollisionFree(\(q_{near},q_{new}\)) then
11: AddNewNode(\(T,q_{new}\));
12:\(R=\text{Distance}(q_{new},q_{goal})\);
13:else
14:\(R=R+k\times\varepsilon\);
15:continue;
16:endif
17:if Distance(\(q_{new},q_{goal})<\rho_{min}\)then
18:return\(T\);
19:endif
20:endfor
21:returnFailed;
```  
**Algorithm 3** CSA-RRT algorithm.  
We initialize the sampling radius \(R\) to \(D_{farthest}\), and the sampling range in the subsequent sampling process is limited to the area within \(R\) of the goal configuration point. If a new node \(q_{new}\) is successfully added in a certain iteration, that is there is no collision with obstacles during the generation of \(q_{new}\), then the value of \(R\) is changed to the distance between \(q_{new}\) and \(q_{goal}\). On the contrary, if there is a collision, then the sampling radius is increased as follows:  
\[R=R+k\times\varepsilon, \tag{2}\]  
where \(k\) is the coefficient used to change the range of the sampling domain; its value is a positive integer and can be adjusted according to the complexity of the environment. \(\varepsilon\) is the step size of the extension. We can find that, if there is no obstacle in the environment or there are obstacles, but no collision, the sampling domain will be reduced to an area \(\parallel q_{new}-q_{goal}\parallel\) away from the goal node each time a new node is added to the tree; once obstacles are encountered during expansion, the sampling domain will be expanded to find a new sampling point and the corresponding \(q_{near}\) node. When \(q_{new}\) is successfully added, the random tree will be restored to the no obstacle state and continue to explore. This gradual change in the sampling area will drive the tree to grow continuously toward the goal node. Figure 1 shows the performance comparison of Basic-RRT and CSA-RRT in the same environment. Obviously, the latter has much fewer nodes. The specific experiment and analysis are provided in Section 3.
## 3 Mathematical Formulation
### Digital Twins
#### 3.1.3 Dt-Sess  
This model accurately simulates the internal states of the SESS, as well as the operational constraints. As detailed in [38], this device has a lot of potential for optimizing energy management strategies, enhancing adaptability to system dynamics.  
The physical layer of the SESS consists of a pack composed of repurposed battery cells, coupled with an AC/DC converter. The battery model employs an ECM, where the open-circuit voltage, represented as \(OCV(z_{k})\), is in function of the SOC, denoted as \(z_{k}\). The electrical impedance of the pack is modeled through a combination of resistive-capacitive (RC) branches in series with a pure ohmic resistance. These parameters capture both the instantaneous voltage drop and the slower transient dynamics. The SOC evolution over time is governed by (3)  
\[z_{k+1}=z_{k}-\eta\cdot i_{k}\cdot\frac{\Delta t}{Q}, \tag{3}\]  
where \(i_{k}\), in A, is the current at time step \(k\), \(\eta\), in p.u., represents the Coulombic efficiency, and \(Q\) denotes the rated capacity of the battery in As. The current through the resistive part of the RC branches follows  
\[i_{Rj,k+1}=e^{-\frac{\Delta t}{N_{j}/c}}i_{Rj,k}+\left(1-e^{-\frac{\Delta t}{N _{j}/c}}\right)i_{k}, \tag{4}\]  
\(R_{j}\) and \(C_{j}\) characterize the specific impedance of each branch, and \(j\) is the branch index. Thus, the battery terminal voltage \(v_{k}\) is obtained through  
\[v_{k}\ =OCV(z_{k})-R_{0}\cdot i_{k}-R_{1}\cdot i_{R_{1},k}-R_{2}\cdot i_{R_{2},k}. \tag{5}\]  
The AC/DC converter model ensures power flow alignment with optimization set-points while maintaining system stability. The DT interacts with real-time measurement systems to continuously update internal states, preventing unsafe operating conditions. Suppose the battery voltage, grid frequency, or current exceeds safe operational thresholds. In that case, an automatic disconnection mechanism is triggered, and an error flag is sent to the DT to halt further updates, thus preventing erroneous optimization decisions.