esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Sample Preparation and Measurement Techniques  
High-quality BSCCO single crystals with a transition temperature \(T_{\rm c}\) of 88.6 K were grown by a floating-zone technique. The main steps of the fabrication process are shown in Figure 1a-d. The process is similar to that reported in Ref. [24]. Here, we describe this process in more detail. First, a piece of a BSCCO single crystal was fixed on a weakly adhesive double-sided tape on the holder of an evaporator. Immediately after cleaving the crystal with another tape, a 120 nm-thick gold film was deposited on its upper surface, as shown in Figure 1a. Next, the crystal with the gold layer was removed from the double-sided tape by a thin wooden sheet and turned over. The sample was then glued onto a sapphire substrate by a thin layer of polyimide. After two hours of drying at 110 \({}^{\circ}\)C, the excess glue on the edge of the sample was removed to allow a further cleaving step by tape. Immediately after cleaving, the fresh surface was covered with a 120 nm-thick gold layer, forming the GBG structure (see Figure 1b). Using photolithography, a 240 \(\times\) 240 \(\upmu\)m\({}^{2}\) square pattern on the mask was transferred to the gold layer, and then the sample was etched down to the lower gold layer by ion milling, as shown in Figure 1c. To pattern the bottom electrode, we manually used a whittled toothpick to paste the photoresist onto the bottom gold film and the stack and then removed the unprotected gold layer by ion milling. After the ion milling process, the photoresist was removed in an acetone solution. The final BSCCO stack on the sapphire substrate is shown in Figure 1d, with lateral dimensions of 240 \(\times\) 240 \(\upmu\)m\({}^{2}\) and a thickness of about 1.8 \(\upmu\)m, corresponding to about 1200 IJJs in the stack. Finally, polyimide was pasted manually to protect the side walls of the stack from shorts, and silver epoxy was used to connect two gold wires to the electrodes.  
After microfabrication, the sample was glued onto a hemispherical sapphire lens with a diameter of 6 mm and placed into a helium flow cryostat with a controlled temperature from 4.2 K to room temperature. The terahertz emission from the BSSCO stack was detected with a TYDEX GC-1P Golay cell, and the emission frequency was measured by a terahertz interferometer [35]. The frequency resolution of the interferometer was about 7.5 GHz, which is calculated by \(\Delta f=c/(2d_{max}\cos\theta)\), where \(d_{max}\) = 20 mm is the maximum differential displacement of the lamellar mirror, \(\theta\approx 4.6^{\circ}\) is the angle of incidence at the lamellar mirror and \(c\) is the speed of light. Here, we should also note that the device reported here is the pristine stack, and sample 1 reported in Ref. [11] is a modified device, where all the side walls were protected by the polyimide, and a 100 nm gold film was evaporated on the top of the stack for reflecting the terahertz radiation into the lens.
## 4 Methodology
### Inversed Distance Attention  
The proposed inversed distance attention is shown in Figure 1b. We can acquire the spatio-temporal embedding vectors of unlabeled grids and all labeled grids through the first layer feature extractor. However, not all labeled grids have the same contribution to each unlabeled grid. The attention mechanism [24] is a beneficial structure that computes their different contributions. Hence, we apply the attention technique in our model to learn the importance between unlabeled grids and all labeled grids. There are a total of \(k\) embedding vectors of labeled grids. Generally, attention networks calculate the attention score of two vectors: \(e_{U}^{2}\) and \(e_{S}n^{2}\) for \(k\) times. Then we exploit each attention score as the pooling coefficient to multiply \(e_{Sn}^{2}\) and sum up the results of all grids as one embedding vector.  
Inner product, cosine similarity, and multilayer perceptron (MLP) are common methods to calculate the attention score. In our model, we make use of MLP as the base attention network because its performance is the best of common methods. In Equation (5), we first concatenate \(e_{U}^{2}\) and \(e_{Sn}^{2}\) and use two dense layers as MLP attention mechanism to obtain the correlation values.  
\[e_{ATTn}=ReLU(W_{ATT}^{2}(ReLU(W_{ATT}^{1}(e_{U}^{2}\oplus e_{Sn}^{2})+b_{ATT }^{1}))+b_{ATT}^{2}),\text{for }n=1,\ldots,k \tag{5}\]  
\[a_{Sn}=\frac{exp(e_{ATTn}*X_{Sn}^{ID})}{\sum_{n=1}^{k}exp(e_{ATTn}*X_{Sn}^{ID} ))},\text{for }n=1,\ldots,k \tag{6}\]  
\[e_{S}^{3}=\sum_{n=1}^{k}e_{Sn}^{2}\times a_{Sn} \tag{7}\]  
Generally, the attention score can be obtained by directly encoding all correlation values by softmax layer. However, the distance between \(G_{U}\) and \(G_{Sn}\) might have a high influence when calculating the correlation. It is conceivable that the closer to \(G_{U}\) in \(G_{Sn}\), the more impact there will be on \(G_{U}\), so in Equation (6) we multiply the inversed distance feature (\(X_{Sn}^{ID}\)) and correlation values before the softmax layer. We can have an attention score for each labeled grid after the softmax layer, and use them as a weighted coefficient to obtain the final output \(e_{S}^{3}\) of inversed distance attention in Equation (7).
## 3 Trajectory Planning of Aerial Robotic Manipulator
### Parametrization of Joint Trajectories  
The trajectory planning problem in Equation (23) is studied as a nonlinear programming (NP) problem. The NP problem considers functional requirements of UAV disturbance and EE manipulability, equality constraint of AEROM forward kinematics, and inequality constraints of joint motions. Each joint trajectory is depicted with a Bezier curve [34; 35]. The Bezier curve contributes to modelling smooth curves in computer graphics, formulated as:  
\[q_{i}(u)=\sum_{j=0}^{m}b_{j,m}(u)P_{ij} \tag{24}\]  
where \(P_{ij}\) is predefined points for the construction of a Bezier curve, \(b_{j,m}(u)\) is the Berstein basis polynomial function as followed:  
\[b_{j,m}(u)=C_{m}^{j}(1-u)^{m-j}u^{j},\quad u\in[0,1] \tag{25}\]  
where \(C_{m}^{j}\) denotes a binomial coefficient, \(C_{m}^{j}=\dfrac{m!}{j!(m-j)!}\). The symbol \(u\) denotes the normalization of time. The AEROM maneuver time is depicted as \(T=t_{f}-t_{0}\). Combined with the form \(u=\dfrac{t}{T}\), the joint trajectory is formulated as:  
\[q_{i}(t)=\sum_{j=0}^{m}C_{m}^{j}\left(1-\dfrac{t}{T}\right)^{m-j}\left(\dfrac{ t}{T}\right)^{j}\!P_{ij} \tag{26}\]  
The angular velocity is:  
\[\dot{q}_{i}(t)=\dfrac{\mathrm{d}q_{i}}{\mathrm{d}u}\dfrac{\mathrm{d}u}{\mathrm{ d}t}=\dfrac{m}{T}\sum_{j=0}^{m-1}b_{j,m-1}\left(\dfrac{t}{T}\right)\left(P_{i,j+1 }-P_{ij}\right) \tag{27}\]  
In this paper, the quintic Bezier curve is employed to parametrize joint trajectories, i.e., \(m=5\). \(\boldsymbol{p}_{i}=[P_{i1},\ldots,P_{5}](i=1,\ldots,n)\) are designed as variables of \(i\)th joint trajectory.  
According to aforementioned equations from (19) to (27), the elements \(P_{ij}(i=1,\ldots,n;j=0,1,\ldots,5)\) are calculated as:  
\[\left\{\begin{array}{l}P_{i,0}=P_{i,1}=P_{i,2}=q_{i}^{0}\\ P_{i,3}=P_{i,4}=P_{i,5}\end{array}\right. \tag{28}\]  
In Equation (28), the value \(q_{i}^{0}\) is predefined. Once the parameter \(P_{i,5}\) is derived, \(\mathbf{p}_{i}\) is determined. Namely, the \(i\)th joint trajectory is obtained.
## 2 Materials and Methods
### Mathematical Model  
In this section, an integrated operation and maintenance scheduling model was developed. The sets, indices, variables, and parameters used in the proposed model are summarized in Table 1.  
After defining the notations required for model development, the constraints and objective functions will be formulated below:  
The following constraint limits the number of times a plant \(i\) has to be maintained during the planning horizon:  
\[\sum\
olimits_{t\in T}St_{it}=u_{i}\quad\forall\;\;i\in\{N1,N2\} \tag{1}\]  
The efficiency of the maintenance at time-period intervals is implemented by equating the corresponding binary variables using the following equation where fi represents the length of time between each maintenance activity for a plant \(i\). For instance, if a particular plant \(i\) has been maintained at time \(t\), the second maintenance task will be performed at time \(t+f\):  
\[St_{it}=St_{i\;t+f_{i}}\;\;\;\forall\;\;i\in\{N1,N2\},t\in T \tag{2}\]  
The size of the crew needed to perform maintenance jobs in plant \(i\) should not exceed the available manpower. The following constraint is added to ensure that there should be no maintenance task for the same crew while they are maintaining other plants. In addition, no more than a fixed number of plants can be served at the same time due to the limited number of crew available. This constraint can be represented mathematically as follows:  
\[\sum\
olimits_{\tau=t-n_{i}+1}^{t}St_{i\tau}\;R_{it}\leq W_{it}\;\;\;\forall \;\;i\in\{N1,N2\},t\in T \tag{3}\]  
The material balance of the plants belong to the first level of HCSC (oil processing plant and gas plants) are represented by Equation (4) where the material flow from the upstream is equal to the the production volumes at each plant \(i\). The processed quantities are then sent to the next level of the HCSC (refinery plants; N2, fractionation plant; N2 and demand node; N3) to satisfy the local and international demands:  
\[\sum\
olimits_{tr}w_{it}^{p}\;Y_{it}^{\tau}=\sum\
olimits_{j\in N2}X_{ijt}^{ p}+\sum\
olimits_{j\in N3}X_{ijt}^{p}\;\;\;\forall\;\;t\in T,p\in P,\;i\in N1 \tag{4}\]
## 1 Introduction
### Motivations  
In recent years, deep learning has made remarkable achievements in signal processing, particularly demonstrating excellent performance in AMC. [PERSON] et al. [14] introduced a Convolutional Neural Network (CNN) model for AMC, which utilized temporal data for training and employed a softmax classifier to classify the extracted features. This approach surpassed the traditional methods that heavily rely on handcrafted features. [PERSON] et al. [15] proposed a new architecture with several specific convolutional blocks, achieving concurrent learning of spatial and temporal signal correlations. [PERSON] et al. [16] implemented an improved CNN with additional layers to adapt estimated delays. In addition, other researchers not only focus on CNN structures, but also explore the advantages of Recurrent Neural Networks (RNNs) in AMC. In [17], a novel data-driven AMC model based on Long Short-Term Memory (LSTM) is proposed. This research demonstrated that LSTM-based models can accurately represent temporal sequences of different durations, which was highly valuable for modulation classification.  
Nonetheless, the direct utilization of machine learning methods to accomplish AMC still presents two noteworthy challenges:  
* When the raw and unprocessed signal is directly inputted, the model primarily conducts feature extraction on the original signal. However, the features derived through this approach often encapsulate only a fraction of the original signal's characteristics, lacking a comprehensive and efficient capability for fulfilling the AMC task.
* Traditional machine learning relies heavily on data-driven pattern recognition and feature extraction, necessitating a substantial pool of well-labeled signal samples. Insufficient training samples can subsequently hamper the model's generalization performance. In practical application, the intricate and varied nature of communication signals makes accumulating and labeling a substantial number of samples more complex. Frequently, only a limited number of samples are available, engendering a scenario where the model's utilization of traditional machine learning-based methods might yield predictions with low confidence when it persists in conducting the AMC task under these constraints.  
Consequently, our focus centers on the resolution of these two challenges.
# UAV Dynamic Non-Terrestrial Transmission Channel Analysis Based on SSCM-RT Model
## 4 Spatial Consistency of Dynamic Channels  
Two asymmetric motion models are used in this section in order to further confirm the reliability of the SSCM-RT model.
# Zero-Shot Recommendation AI Models for Efficient Job-Candidate Matching in Recruitment Process
## 2 Materials and Methods
### Evaluation Metric: Top-K Accuracy  
In the framework of our investigation into job-candidate matching utilizing Zero-Shot Recommendation AI Models, we implement the \"Top-K Accuracy\" metric as a crucial measure of performance. This metric plays a key role in determining the model's ability to accurately rank candidates based on their relevance to a specific job position.  
Top-K Accuracy is a fundamental metric in recommendation systems and information retrieval fields. It measures the model's effectiveness in identifying the true candidate for a job within the top K candidates, ranked according to their relevance scores. These relevance scores can be derived from cosine similarity or dot product calculations between the job description and the candidate profiles. Both metrics serve to quantify the alignment in a high-dimensional feature space, albeit through slightly different mathematical approaches.  
The process to compute Top-K Accuracy for each job in our dataset involves several steps:  
1. Rank candidates based on their relevance scores to the job description, with scores calculated using either cosine similarity or the dot product method, from highest to lowest.
2. Check whether the true candidate--the one who was actually hired or is deemed the most suitable for the job--is among the top K-ranked candidates.
3. A correct prediction is noted if the true candidate is within this top K group.  
Accordingly, Top-K Accuracy is determined by the ratio of correct predictions to the total number of jobs assessed. This can be formulated as  
\[\text{Top-K Accuracy}=\frac{\text{Number of Jobs with True Candidate in Top K}}{\text{Total Number of Jobs}} \tag{3}\]  
Applying Top-K Accuracy in our analysis provides insight into the model's practical effectiveness in recruitment contexts. High Top-K Accuracy signifies the model's proficiency in narrowing down the pool of candidates, thereby facilitating the recruitment process and increasing the probability of successful job--candidate pairings. This metric is especially pertinent for evaluating the model's precision in initial candidate screenings within large applicant pools, where the ability to effectively shortlist candidates is essential.
# Entity Alignment Method Based on Joint Learning of Entity and Attribute Representations
## 2 Materials and Methods
### Embedding Learning
#### 2.3.2 Attribute Character Embedding Learning  
For learning attribute character embeddings, we first define a character sequence for attribute values, \(v=\{v_{1},v_{2},\cdots,v_{t}\}\). The N-gram-based compositional function is then used to encode the character sequence of the attribute values.  
\[f_{a}(\mathbf{v})=\sum_{n=1}^{N}\left(\frac{\sum_{i=1}^{t}\sum_{j=i}^{t}\mathbf{v}_{j}} {t-i-1}\right) \tag{10}\]  
where \(\mathbf{v}_{1},\mathbf{v}_{2},\cdots,\mathbf{v}_{t}\) are the character embeddings of the attribute values.  
Based on the TransE model, the attribute \(a\) in \((e,a,v)\) is the entity-to-attribute value mapping. In other words, adding attribute vector \(\mathbf{a}\) to entity vector \(\mathbf{e}\) yields the attribute-value vector \(f_{a}\left(\mathbf{v}\right)\). The learning of attribute character embeddings is performed by minimizing the loss function \(L_{CE}\), given as:  
\[L_{CE}=\sum_{(e,a,v)\in T_{A}}\sum_{(e^{\prime},a^{\prime},e^{\prime})\in T^{ \prime}_{A}}\Bigl{[}\gamma+\left\|\mathbf{e}+\mathbf{a}-f_{a}(\mathbf{v})\right\|_{L_{2}} -\left\|\mathbf{e}^{\prime}+\mathbf{a}^{\prime}-f_{a}\left(\mathbf{v}^{\prime}\right) \right\|_{L_{2}}\Bigr{]}_{+} \tag{11}\]  
where \(T_{A}\) is the set of valid attribute triples and \(T^{\prime}_{A}\) is the set of corrupted attribute triples, where \(T^{\prime}_{A}=\{(e^{\prime},a,v)|e^{\prime}\in E\}\cup\{(e,a,v^{\prime})|v^{ \prime}\in V\}\cup\{(e,a^{\prime},v)|a^{\prime}\in A\}\).
## Appendix A Definitions  
This appendix defines the main graph theory concepts used in this paper. Basic concepts not defined here follow the definitions of [PERSON] [42].  
**Definition 1** (Graph, \(G=G(V,E)\)).: _A graph \(G=G(V,E)\) consists of a set \(V=V(G)\) of vertices or nodes, and a set \(E=E(G)\) of edges or links, where each edge \(uv\) connects a pair of vertices \(u,v\in G\). The order of \(G\) is \(n=n(G)=|V(G)|\), and the size of \(G\) is \(m=m(G)=|E(G)|\)._  
**Definition 2** (Connected graph).: _A graph \(G\) is connected if there is at least one path between each pair of vertices \(u,v\ \in V(G)\)._  
**Definition 3** (\(k\)-connected graph).: _A graph is \(k\)-connected if and only if there are at least \(k\) vertex-disjoint paths between each pair of vertices \(u,v\ \in V(G)\)._  
**Definition 4** (Vertex degree).: _The number of edges incident to a vertex \(v\in V(G)\) defines the degree of \(v\)._  
**Definition 5** (Geodesic or shortest path).: _The shortest path (in number of hops) connecting two vertices \(u,v\in V(G)\) is called a uv geodesic._  
**Definition 6** (Distance between two vertices).: _The distance between two vertices \(u\) and \(v\) in a connected graph \(G\), denoted as \(dist(u,v)\), is the length of a geodesic between \(u\) and \(v\) in \(G\)._  
**Definition 7** (Graph distance matrix).: _Let \(G\) be a connected graph of order \(n\). The distance matrix of \(G\) is an order \(n\) matrix such that:_  
\[Dist[i,j]=\begin{cases}dist(v_{i},v_{j}),&v_{i},v_{j}\in V(G),\ i\
eq j;\\ 0,&\text{otherwise.}\end{cases} \tag{1}\]  
**Definition 8** (Graph diameter).: _The diameter of a connected graph \(G\) is the length of the greatest geodesic in \(G\). More formally, it is given by:_  
\[diam(G)\ =\max\{dist(u,v);\ u,v\ \in V(G)\}. \tag{2}\]  
**Definition 9** (Vertex transmission [29]).: _Let \(G\) be a connected graph. The transmission of a vertex \(v\ \in V(G)\) is defined as:_  
\[T(v)\ =\sum_{u\in V(G)}\mathit{dist}(u,v).\] (A3)  
**Definition 10** (Graph Wiener index [26]).: _Let \(G\) be a connected graph. The Wiener index of \(G\) is given by:_  
\[W(G)=\sum_{u\in V(G)}\ \ \sum_{v\in V(G),\ v<u}\ \ \mathit{dist}(u,v).\] (A4)  
**Definition 11** (Graph average distance).: _The average distance of a connected graph \(G\) is the Wiener index of \(G\) over the number of pairs of vertices in \(G\)._  
**Definition 12** (Node Wiener impact).: _Let \(G\) be a 2-connected graph. The Wiener impact of a vertex \(v\ \in V(G)\) is defined as:_  
\[\tau_{v}\ =W(G-v)-W(G)+T(v),\] (A5)  
_where \(G-v\) refers to the graph obtained by removing \(v\) from \(G\)._  
**Definition 13** (Edge betweenness).: _The betweenness of an edge \(uv\in E(G)\) is given by:_  
\[b_{uv}\ =\sum_{k\in V(G)}\ \ \sum_{l\in V(G),\ k<l}\ \ \frac{s_{uv}^{kl}}{s^{kl}} ;\ \forall\ uv\in E(G),\] (A6)  
_where \(s_{uv}^{kl}\) is the number of geodesics between vertices \(k\) and \(l\) (\(k<l\)) passing through edge \(uv\), and \(s^{kl}\) is the total number of geodesics between \(k\) and \(l\)._
## 4 Methodology
### Feature Engineering
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Equation** & **Description** \\ \hline Mean & \(\overline{X}=\frac{1}{n}\sum\limits_{i=0}^{n}\left(X_{i}\right)\) & The mean of the feature was calculated for each subject for all the samples in each fold i.e., 33 samples. \\ \hline Standard Deviation & \(\sigma=\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}\left(X_{i}-\overline{X}\right) ^{2}}\) & The standard deviation of the signal is calculated for each subject for all the samples in each fold i.e., 33 samples. \\ \hline Median Absolute Deviation & \(\begin{array}{c}MAD=median\left(\left|X_{i}-\overline{X}\right|\right)\end{array}\) & The median absolute deviation was calculated each subject for all the samples in each fold i.e., 33 Samples. \\ \hline Minimum & min(X) & The minimum of the signal was for calculated each subject for all the samples in each fold i.e., 33 Samples. \\ \hline Maximum & max(X) & The maximum of the signal was for calculated each subject for all the samples in each fold i.e., 33 Samples. \\ \hline Energy Measure & \(E_{x}=\frac{1}{n}\sum\limits_{i=0}^{n}\left|x[i]\right|^{2}\) & The energy measure of the signal was calculated for each subject based on all the samples in each fold i.e., 33 Samples. \\ \hline Inter Quartile Range & \(IQR=median(Q_{3})-median(Q_{1})\) & The interquartile range was calculated on each subject by considering each fold that is 33 samples of data as the complete set. \\ \hline Signal Magnitude Area & \(SMA=\frac{1}{n}\sum\limits_{i=0}^{n}\left(\left|X_{i}\right|+\left|Y_{i} \right|+\left|Z_{i}\right|\right)\) & The signal magnitude area is been calculated by considering all the axis of the accelerometer and gyroscope and is calculated for each subject by considering the samples in each fold. \\ \hline Skewness & \(Skew(X)=\frac{n}{(n-1)(n-2)}\sum\limits_{i=0}^{n}\left(\frac{X_{i}-\overline{X} }{s}\right)\) & The skewness for all the samples of a fold for a particular subject was calculated on the basis of the Fisher-Pearson standardized moment coefficient \\ \hline Kurtosis & \(Kurt(X)=\frac{E\left[\left(X-\overline{X}\right)^{4}\right]}{std(X)^{2}}-3\) & The kurtosis (peakedness) is calculated for individual signals. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical features.
## 5 Discussion
### Policy and Market Barriers to Digestate Utilization
#### 5.5.3 Challenges and Uncertainties in the Legal Framework for Digestate  
Despite the existence of both harmonized EU regulations--such as Regulation (EU) 2019/1009 [124]--and national legislative frameworks like the Spanish law, the legal status of digestate remains complex and often ambiguous. Its classification as a product, by-product, waste, or end-of-waste material varies across jurisdictions, creating uncertainty for producers, regulators, and market actors. The coexistence of multiple regulatory pathways, along with regional restrictions and variability in feedstock inputs, complicates the commercialization and application of digestate--particularly for smaller operators. While some successful cases exist, they remain limited, underscoring the pressing need for clearer, more consistent guidance and regulatory harmonization. Addressing these challenges is essential to unlocking digestate's potential as a sustainable fertilizer and advancing its role in circular economy strategies and climate-resilient agriculture.  
In conclusion, the environmental performance of digestate is shaped by a combination of technical, methodological, and regulatory factors. Variables such as feedstock selection, system boundaries, functional units, post-treatment strategies, and allocation methods significantly influence the outcomes of Life Cycle Assessment studies. Although methodological heterogeneity limits direct comparability, it also underscores the importance of standardized approaches and context-specific interpretation. Additionally, regulatory complexity and market barriers remain critical obstacles to the large-scale adoption of digestate-based solutions. A more harmonized legal framework, coupled with policy incentives and continued technological innovation, will be essential to unlock the full environmental and agronomic potential of digestate within circular economy strategies.