esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
In the discussion, we place a strong emphasis on GWP; nevertheless, all CML2001 (August 2016) midpoint indicators are assessed. It is important to note that the output of concrete demolition can vary greatly, from the result of large fragments, which can be used as crushed stone in road construction, as well as fine grains, which can be used again directly in fresh concrete. Depending on the grain size, the required energy and fuel consumption, and thus the resulting emissions, also vary. Therefore, we list below a range of possible emissions from concrete crushing, which range up to a fineness of 0/1 mm. Considering the GWP in kg CO2e, aligning the concrete fraction for the CRC double wall results in a range of 2-14 kg CO2e (Figure 5); depending on the processes, the final grain size and required energy can be assumed (and the mass can be demolished). Mechanical recycling results in 4.9 kg CO2e. In contrast, pyrolysis produces 18.7 kg CO2e per double wall. Adding the concrete fraction and the two fiber EoL scenarios, the results in kg CO2e per double wall from carbon reinforcement range from 7 kg CO2e for concrete crushing and mechanical recycling to 22.4 kg CO2e for concrete crushing and pyrolysis (Figure 6) (including different concrete demolition ranges). Figure 5: Concrete demolition range—depending on the grain size and process used. Figure 6: GWP per double wall CRC. Considering the individual EoL steps of the fibers (mechanical recycling and pyrolysis), the main drivers in mechanical recycling are the energy required (70%) and transport (30%) (Figure 7). For pyrolysis, 84% of the total GWP (18.7 kg CO2e) can be attributed to the required electricity, whereas only 8% can be attributed to transport and another 8% to gas (Figure 7). For steel recycling (Table 2), we assume that the crushed concrete can vary in the resulting grind sizes. We assume identical crushing steps to the ones for CRC (Figure 3). However, the results differ from those of the CRC due to a higher amount of concrete in SRC. The concrete demolition for the reinforced concrete is at a GWP of 6.9 kg CO2e with a particle size of 8/22 mm (Scenario A). The treatment by a conventional stationary plant (scenario B1) leads to 9.5 kg CO2e. The innovative stationary plant produced the finest grains (Scenario B2), which can be used directly in fresh concrete. Consequently, the largest amount of energy is required for crushing, which is also reflected in a GWP of 14 kg CO2e, the highest GWP for concrete crushing scenarios. For the steel and its EoL, we consider only the detachment from the surrounding concrete, as well as the transport of steel to the nearest steel mill, for melt down and recovery of 100% of steel. The melting itself is not included in the case study because after the steel is detached from the concrete, a reusable material already exists (goal: re-usable material out of double wall). Consequently, the GWP for the predefined steel-reinforced concrete double wall ranges from 5.6 kg CO2e to 15.9 kg CO2e, depending on the processes used--scenarios A, B1, or B2 (Figure 8).
## 7 Challenges and Limitations
### Signal Interference and Electromagnetic Compatibility  
RF energy harvesters operate in environments crowded with numerous communication signals. In such settings, electromagnetic compatibility (EMC) and signal interference become serious issues. Harvesters may unintentionally pick up noise or unwanted signals, degrading the efficiency of energy capture and potentially causing harmful interference to nearby sensitive communication systems [112]. Studies have reported that rectifiers and matching circuits can experience significant energy conversion degradation when exposed to wideband noise. Moreover, the presence of harmonics generated by the rectification process can interfere with nearby RF communication systems if proper harmonic filtering is not implemented [113]. The root causes of these issues include the broadband nature of ambient RF sources, the non-linear behaviour of rectification circuits, and insufficient electromagnetic isolation between components. Rectifiers operating over multiple frequency bands tend to lack selectivity, making them prone to harvesting irrelevant or noisy signals. Additionally, poorly matched or untuned circuits may reflect the transmission of energy into the environment, further increasing electromagnetic pollution [112]. Technical solutions to mitigate these issues include the integration of bandpass or notch filters that selectively allow the attainment of desired frequencies while attenuating out-of-band noise and harmonics. Shielded enclosures or multilayer PCB designs with ground planes can also be employed to improve isolation and minimize cross-talk. Moreover, adaptive interference cancellation techniques based on software-defined radio (SDR) and digital signal processing (DSP) have been proposed to dynamically suppress unwanted signals in real time. These methods enable rectifier circuits to adapt their behaviour based on the spectral environment. Recent developments also explore using reconfigurable impedance matching networks to reduce sensitivity to broadband interference and optimize energy extraction from clean, narrowband sources. By incorporating machine learning algorithms, these networks can autonomously detect and respond to spectral anomalies that would otherwise degrade performance [113]. The management of this coexistence between energy-harvesting devices and traditional RF communication infrastructures without introducing mutual interference remains an open research problem, requiring sophisticated filtering, shielding, and adaptive control techniques.
# A New Coding Paradigm for the Primitive Relay Channel +
## 5 Conclusions  
We have proposed a new coding paradigm for the primitive relay channel that combines compress-and-forward and decode-and-forward by means of a chaining construction. The achievable rates obtained by our scheme surpass the state-of-the-art coding approaches (compress-and-forward, decode-and-forward, and the soft decode-and-forward strategy of [25]). Our coding paradigm is general in the sense that we treat decode-and-forward and compress-and-forward as existing primitives. For this reason, any coding scheme that can be used to implement decode-and-forward/ compress-and-forward can also be used to implement our new strategy. Polar codes are one notable  
Figure 4: Comparison between the achievable rate provided by our strategy and the existing upper and lower bounds. We use “CF” and “DF” as abbreviations for “compress-and-forward” and “decode-and-forward”, respectively.  
example, since polar coding schemes for decode-and-forward and compress-and-forward have been developed; see [20; 21; 22; 23; 24]. This leads to a scheme with the typical attractive features of polar codes, i.e., quasi-linear encoding/decoding complexity and fast decay of the error probability. A detailed analysis of the finite length performance of polar codes for our strategy (as well as of polar codes for decode-and-forward and compress-and-forward) is an interesting direction for future research.  
In the numerical simulations, we consider the special case of the erasure relay channel. In this setting, the upper bounds presented in Section 2.2 do not provide an improvement over the cut-set bound. An interesting avenue for future work is to study the performance of our strategy in scenarios where the cut-set bound is not tight (e.g., as in [11; 12; 35]). For example, in [35], the model also includes a state sequence, and the partial decode-compress-and-forward strategy crucially takes advantage of it by optimally adapting its transmission to the dependence of the orthogonal channels on the state sequence. In this paper, we do not consider such a state sequence, and it is not obvious how to adapt our results to the model of [35].  
Conceptualization, M.M., S.H.H. and R.U.; Formal analysis, M.M., S.H.H. and R.U.; Funding acquisition, M.M. and R.U.; Supervision, R.U.; Visualization, S.H.H.; Writing--review & editing, M.M.  
[PERSON] was supported by an Early Postdoc.Mobility fellowship from the Swiss NSF. [PERSON] was supported by Grant No. 200021_156672/1 of the Swiss NSF.  
The authors declare no conflict of interest.
## 3 Numerical Simulation Results
### Analysis of Numerical Results  
A2, B2, C2, D2, and plain concrete (PC) were chosen as the supporting materials of the roadway, and their physical and mechanical parameters are listed in Table 4. The distribution characteristics of the plastic zone in the surrounding rocks and the deformations in the rigid support structures were evaluated under different supporting materials. Five cases with different supporting materials were considered, and the specific supporting ways were as follows: after the 5 m excavation, at the vicinity of the rock wall, the supporting material of 300 mm thickness was first, poured and served as the internal supporting structure, and then followed by the PC of 100 mm thickness as the outer rigid supporting structure. The supporting schemes are listed in Table 5. The stress-displacement curve of the PC under the uniaxial test is shown in Figure 8, and the strain-softening equation of PC is given in Equation (8). It is worth noting that, due to the need to consider the strain-softening process of supporting materials, the PC adopted in the numerical simulation was A1, instead of C40 concrete. Moreover, in this study, because the proportion of PC was not optimal, the uniaxial compressive strength of PC was only 15.43 MPa, and the elasticity modulus of the PC given in Table 4 was low compared to that of the normal weight concrete. However, the outer rigid supporting materials in all cases were the same PC, so the lower elasticity modulus of the PC almost did not affect the comparison among different cases.  
\[\sigma_{p}=\left\{\begin{array}{rl}20.98-12.33d,&0.45\leq d<0.85\\ 8.65,&d\geq 0.85\end{array}\right. \tag{8}\]
## 3 Machinability of AM Ti6 Al4V Alloy
### Material of a Cutting Tool
* Special-purpose tools (up to 4500 HV) such as cubic boron nitride and polycrystalline cubic boron nitride with a maximum cutting speed of 800 m/min, a polycrystalline diamond with a top cutting speed of 1300 m/min. These are the less applicable cutting tools in the machining industry because of their high tool cost, low toughness, and high hardness compared to other cutting tools, showing that ceramic and carbide tools perform better at high cutting temperatures. Several reports indicate that straight tungsten carbide (WC/Co) tools have demonstrated superior performance in nearly all machining processes involving titanium alloys. These tools offer enhanced wear resistance and durability, making them well-suited for the challenges associated with machining titanium [42; 51; 54]. It was indicated that these WC/Co alloys with a Co concentration of 6 Wt% and a minimum WC grain size of between 0.8 and 1.4 m might achieve the best performance [54]. The carbide tool with H13A grade type K20 was found to perform excellently in the titanium alloy turning operation [3; 53]. The cutting material must have the following properties when machining difficult-to-cut material (like titanium alloy):  
* Excellent chipping resistance
* Hot hardness, necessary to maintain the high cutting temperature
* Good compressive strength
* Fatigue resistance and high toughness value, to withstand the cyclic machining forces generated
* Low chemical affinity with the titanium atom
* Outstanding thermal conductivity, to dissipate the heat generated quickly. The limited machinability of titanium alloys is primarily due to their high reactivity with most cutting tool materials. Performing cutting operations in an inert environment has shown to enhance tool life by reducing chemical interactions between the tool and the material, even in conventional machining settings. However, during the machining of titanium alloys, the high-temperature accumulation between the tip of the workpiece, cutting tool, and chip results in tool coating failure and delamination [39]. The heat partition during machining titanium alloys was observed to be 80% transferred to the cutting tool, while only 20% was transferred to the chips [52]. Therefore, it might cause frequent loss or change of cutting tools, which leads to high machining costs. Due to the high machining costs in traditional processes, titanium alloys have drawn the utmost interest in metal additive manufacturing, reducing the cost in return [55]. Table 5 highlights the mechanical properties of a tungsten carbide tool with high density and excellent thermal conductivity compared to Ti6 Al4V alloy.
## 2 Requirements of 5G-Based V2X Use Cases  
This section explores the requirements of vehicle-to-everything (V2X) use cases focusing on cellular communication. We do not discuss the C-V2X requirements in great detail as there are many other research papers, studies, and recommendations covering this topic. However, a brief overview of the C-V2X requirements is essential for the discussion and understanding of the enablers, recent research, obstacles, and challenges of the C-V2X, which are the core and novelty of this paper.  
The V2X use cases are mainly safety-relevant use cases. Thus, high reliability and availability are the most crucial requirements. In order to design such a system, the V2X protocols are designed to support distributed operation [7; 15; 27]. Among other implications, this design principle ensures that a defective node has only a minor effect on the operation of the whole system. The distributed operation implies that the processing of the shared data and the decisions made (based on the data) are taken in the individual vehicles. This also means that the communication basically shares awareness data. These data include the status, perception, or intention of the ego entity or the status of the infrastructure. The data are broadcasted periodically within a local geographic scope. A certain level of packet loss is usually acceptable since the shared information is repeated (and updated) periodically. This operation also implies that the communication is signaling-typed rather than session-typed [28]. The V2X applications rely on standards specified by ETSI or SAE, depending on the region. In addition to this, the 5 GAA [1; 2; 3] defines requirements for the applications.
# STOD: Towards Scalable Task-Oriented Dialogue System on MultiWOZ-API
## 3 Construction of MultiWOZ-API  
To verify the effectiveness of the proposed framework, we investigate existing task-oriented dialogue datasets. We found that only TicketTalk [23] annotates API query information. However, the dataset is limited to a single domain, which is insufficient for scalability research. Additionally, without a database to process API queries against, obtaining comprehensive results for improving the scalable dialogue policy is not feasible. To this end, we build an API-schema-based multi-domain dataset MultiWOZ-API by re-annotating MultiWOZ2.1 [2], which is a multi-domain task-oriented dialogue dataset and gives a complete database. Specifically, firstly, we define the API schema information of each domain. Secondly, we re-annotate the dataset according to the existing dialogue state information.
# Dynamic Multi-Task Graph Isomorphism Network for Classification of Alzheimer's Disease
## 4 Experiments
### Ablation Study  
In this section, we conducted several experiments to validate the performance of each key component of our DMT-GIN by successively removing critical parts of the model and observing the changes in model performance. Specifically, we conducted ablation studies on the following three aspects.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Hyper-Parameters** & **Accuracy** & **Specificity** & **Recall** & **F1 Score** \\ \hline batch\_size = 32 & lr = 0.0001 & 85.11 & 94.15 & 73.30 & 81.03 \\ batch\_size = 32 & lr = 0.001 & 81.06 & 96.09 & 61.60 & 73.92 \\ batch\_size = 32 & lr = 0.01 & 72.74 & 97.24 & 40.00 & 55.97 \\ batch\_size = 64 & lr = 0.0001 & 76.65 & 87.69 & 61.23 & 68.64 \\ batch\_size = 64 & lr = 0.001 & 90.44 & 95.97 & 83.22 & 89.14 \\ batch\_size = 64 & lr = 0.01 & 81.80 & 92.30 & 62.33 & 74.41 \\ batch\_size = 128 & lr = 0.0001 & 78.86 & 90.25 & 63.98 & 72.42 \\ batch\_size = 128 & lr = 0.001 & 86.58 & 92.85 & 77.47 & 82.49 \\ batch\_size = 128 & lr = 0.01 & 76.83 & 97.46 & 48.47 & 63.79 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The performance of the model under different combinations of batch sizes and learning rates.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Specificity** & **Recall** & **F1 Score** \\ \hline SVM & 83.12 & 82.91 & 83.36 & 79.36 \\ KNN & 70.24 & 72.62 & 67.69 & 64.65 \\ WKNN & 72.58 & 74.31 & 70.43 & 67.80 \\ CNN & 83.24 & 83.54 & 78.21 & 82.19 \\ GCN & 88.93 & 95.94 & 79.82 & 86.25 \\ GAT & 85.66 & 95.42 & 73.10 & 81.69 \\ GWNN & 85.29 & 96.95 & 67.59 & 78.49 \\ TAGCN & 86.02 & 96.12 & 72.64 & 81.73 \\ DMT-GIN & 90.44 & 95.97 & 83.22 & 89.14 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison with the baseline model.
# Bandwidth-Based Wake-Up Radio Solution through IEEE 802.11 Technology
## 1 Introduction
Power saving is another issue that has been considered since the initial IEEE 802.11 standards, which allowed STAs to remain in a Power Saving Mode (PSM) to reduce energy consumption and extend battery time. In PSM, inactive STAs are allowed to move to an energy-saving state (i.e., go to sleep). Meanwhile, the serving AP buffers downlink frames addressed to sleeping STAs. Those STAs wake up periodically to check whether they have pending frames; if not, they can go back to sleep; if yes, STAs poll the AP to retrieve those frames and return to the power-saving state. In the legacy PSM, STAs could remain inactive for several hours, whereas IEEE 802.11 ah allows longer periods of sleep (to the year scale) [4]. IEEE 802.11 ah also introduces the Target Wake-up Time (TWT) mechanism, by which the AP and STAs agree on a specific serving period, thus reducing the time an STA must be awake to check for downlink frames. In a similar way, the RAW mechanism helps in saving energy as well by allowing STAs to sleep outside their assigned transmission window. These power-saving mechanisms, however, suffer from two sources of inefficiency: (i) STAs must wake up periodically, many times unnecessarily if there is no pending traffic for them; and (ii) time-sensitive frames have to wait for possibly long times in AP's buffers before the recipient STA wakes up from PSM, or at TWT. Those inefficiencies can be avoided by the use of Wake-Up Radio (WuR) mechanisms [5]. In a WuR system, energy-constrained networking devices are equipped with a secondary low-power radio, which is always awake, while the main radio is allowed to sleep for long periods. In the presence of downlink traffic, the transmitter sends a Wake-up Call (WuC) addressed to the recipient STA's secondary radio, which will immediately wake up the primary radio in order to start receiving those frames with a minimum delay. IEEE 802.11 ba defines such a system for Wi-Fi 6 devices and beyond, as they are not backward compatible with previous generations of Wi-Fi devices, not even with Wi-Fi 6 devicesalready available on the market. In this paper, however, we define a novel WuR solution, which enables any existing Wi-Fi device capable of transmitting different bandwidths (i.e., from IEEE 802.11n-2009 on) to become a WuR Transmitter (WuTx). We also propose a simple Wake-up Receiver (WuRx) design, which we show, through a comprehensive set of Matlab simulations, that represents a suitable and feasible IEEE 802.11-based WuR solution. The remainder of this paper is structured as follows. Section 2 presents with more detail the concept of WuR and reviews existing literature on IEEE 802.11-based WuR applications. Section 3 describes our proposed solution for a WuR using IEEE 802.11 transmitters, while Section 4 discusses an exemplary implementation of a compatible WuRx. Section 5 proves the feasibility of our approach by means of a simulation-based study, and Section 6 provides concluding remarks.
## 4 DQN-Based Priority/Demanding Resource Management  
### Markov Decision Process Elements  
We study the MDP algorithm to solve the problem, which is a tuple of \((\mathcal{S},\mathcal{A},\mathcal{R},\mathcal{S}^{\prime})\), where \(\mathcal{S}\) denotes the set of the possible states, \(\mathcal{A}\) is denoted the action. At _timeslot-t_, the agent observes the state \(s_{t}\) of the time slot and selects action. In our proposed system, we leverage deep q-network-based-priority/demanding resource management (DQG-PD). We have developed the DQG-PD to enhance the network policy in terms of resource management and efficiency. Our approach obtains the optimal network policy for efficient resource offloading in MEC servers that demonstrate resource capacity in terms of maximizing the energy efficiency in O-RAN-enabled MEC. However, to utilize the DRL approach for reinforcement learning (RL) agents, we address the steps of states-space, action-space, and rewards function as:  
1. State-space: in each time slot, each communication link and computation in MEC observe the network state from the environment. Let \(S\) denote the state space. The current environment state includes \(D_{n,m}(t)\) measurement of the data transmission rate from the IIoT device and MEC server, the status of all resources in the IIoT device \(S^{i}=1\) is supposed to offload the resource to the MEC server, 0 otherwise. \(CB_{n}(t)\) computation task model of _n-th_. As a result, state \(S\) is defined by the following parameters: \[S(t)=\left[D_{1,1}(t),D_{1,2}(t),\ldots D_{n,m}(t),CB_{1}(t),CB_{2}(t),\ldots CB _{n}(t),B_{1,1}(t),B_{1,2}(t),\ \ldots B_{n,m}(t),S^{i}_{n},\right]^{T}\] (14)
2. Action-space: we utilize agents to make decisions based on gathering the current state of the environment. The goal of the agent is to make the optimal decision based on maximizing the resource utilization in terms of bandwidth, computation resource utilization, and minimizing the overall average service delay with minimal task execution. Action \(a(t)\in\mathcal{A}\) at each time step \(t\) can be defined as the action in our network system, which considers offloading the _t-th_ task \((1<t\leq N)\) and allocating the resource (bandwidth and computation resource) to the task for execution on the MEC server. Action can be defined as: \[a(t)=\left\{H_{n,m},\ S_{i},\Psi\right\}\] (15) where \(H_{n,m}\) is a representation of the channel bandwidth, \(S_{i}\) selection task offloading for task size with \(\Psi\) belonging to \(\left\{1,2,\ldots,Y\right\}\) MEC server when \(S_{i}=1\), and \(S_{i}=0\) in local. The agent will take actions based on tasks in each time step and get the reward from the environment. Note that in each decision epoch, an action also affects the next state \(s^{\prime}\) in next time _slot-t_.
3. Reward: RL aims to maximize the reward from good actions. Our reward function is to design and optimize to reflect the enhancement of the priority of resource management and efficient energy. The reward function \(r_{t}\) can be defined as: \[r_{t}=CT^{m,m}_{msc}=\frac{S_{n}(t)}{D_{n,m}}+X^{n,m}_{msc}\] (16)