esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
(1) Background: Individuals with high levels of state depression are hypothesized to have an impairment of attentional control functions necessary for filtering irrelevant information. This study used the event-related potential of early \(\mathrm{P_{D}}\), a marker of distractor suppression, and N2 pc, an indicator of attentional capture to investigate whether high state depression affects selective attention in ignoring or suppressing distractors. (2) Methods: Thirty-three undergraduate students completed the Depression Anxiety Stress Scale-21 (DASS-21) and performed a modified, delayed match-to-sample task. Participants encoded abstract shapes under low or high perceptual load conditions in the visual working memory while ignoring a lateralized Chinese character as a task-irrelevant singleton distractor. (3) Results: Individuals with high state depression failed to suppress the distractor, as evidenced by the absence of early \(\mathrm{P_{D}}\). Under low perceptual loads, they also displayed a significant N2 pc component, indicating attentional allocation to the distractor. In contrast, low-state-depression participants successfully suppressed the distractor, showing early \(\mathrm{P_{D}}\) and the absence of N2 pc. (4) Conclusions: These findings suggest that high-state-depression individuals have an impairment in top-down attentional control, particularly in feature-based selective attention. This deficit hinders the ability to filter out irrelevant information, potentially contributing to cognitive difficulties associated with depression.  
Keywords:state depression; perceptual load; visual attention; suppression; selective attention; top-down control; event-related brain potential; early \(\mathrm{P_{D}}\); N2 pc
## 5 Packet Length Control for Sparse Signal Transmission
### Performance Improvement with Data Interleaving
#### 5.3.1 An Easy-to-Implement Method: Interleaving  
Traditionally, when data loss exists, ARQ is always adopted to retransmit lost packets. However, ARQ does not function well when severe packet collision or communication noise exists with introducing extra energy consumption and delay. There is something else we can do that provides better protection against burst errors: we can transmit data bits in a different order than the order in which the data bits were originally transmitted. Doing so is called interleaving [37]. Adopting data interleaving before data transmission as a feedforward way surely can make the data loss random, thereby improving the sparse signal transmission performance in bursty data loss condition. This approach is based on mixing up or interleaving the order of the sampling sequence before transmission and unmixing or de-interleaving it on reception. That way, if a packet (or burst of packets) is lost, the loss will be spread out over time by the mixing. It will not result in a single, large gap during signal transmission.
## 4 Experimental Results and Analysis
### Comparisons between OCA and GA, SSA, ARO  
In order to test the performance of OCA, we now compare OCA with GA, SSA, and ARO. We use the sub-optimal combination of IMFs obtained by OCA and other algorithms to construct the reflectance templates for velocimetry. The convergence curve of each algorithm is shown in Figure 5. The x-axis represents the convergence iterations of swarm intelligence algorithms, while the y-axis represents the minimum error achieved by each algorithm.  
Figure 4: Comparison of OCA with zone competition and without zone competition.  
Figure 5: Comparison of convergence curves for OCA, GA, SSA, ARO.  
We can see that although OCA, GA, SSA and ARO have the same convergence value, in terms of convergence speed, OCA achieves convergence with the fastest convergence speed in the 3 rd generation. ARO achieves convergence second only to OCA in the 5 th generation, while SSA and GA converge in the 24 th and 38 th generations, respectively. Experimental results demonstrate that OCA has the fastest convergence speed, and the incorporation of OCA mechanisms can enhance algorithm convergence, including the determination of safety zones to reduce the search range and the zone competition to accelerate search efficiency.
## 4 Scenarios
### Application Scenario  
In order to show the benefits of SCHC in real life, we chose to focus on a widely used realistic metering use case implementing DLMS, optionally secured using DTLS and transported either using the IP or non-IP transport mode.  
This results in the eight variants shown in Figure 5. To further examine the protocol stack of each variant, we present the packet structure of each variant in Figure 6. The figure also shows on which part of the packet compression and encryption (when used) are applied. Different payload sizes are considered, ranging from 50 to 200 bytes, which correspond roughly to typical water, gas and electricity metering use cases. Example of typical DLMS patterns are listed below.  
* One message of 100-200 bytes every 15 min (electricity);
* Five messages of 100 bytes per day (water);
* Two messages of 100-150 bytes per day (gas).
## 4 Derivation of the Hybrid Compact Systolic Multiplier
### Projection Function  
The projection function, as detailed in [47], plays a critical role in the optimization of dependency graphs (DGs) by merging multiple vertices \(\boldsymbol{\Gamma}(i,j)\) into a unified processing element (PE), referred to as \(\overline{\boldsymbol{\Gamma}}\). This consolidation simplifies the computational architecture, enabling more efficient processing. Once these PEs are formed, they are interconnected to construct a systolic or semi-systolic array, which facilitates streamlined data flow and computation. The projection function can be represented in different ways, one of which is presented below:  
\[\overline{\boldsymbol{\Gamma}}=\boldsymbol{\Xi}\ \boldsymbol{\Gamma} \tag{14}\]  
In the earlier discussed equation, the projection matrix is represented by \(\boldsymbol{\Xi}\). To effectively ascertain this projection matrix, it is crucial to first determine the corresponding projection vector \(\boldsymbol{\Gamma}\). As noted in [47], the projection vector \(\boldsymbol{\Gamma}\) functions as the null vector of the projection matrix \(\boldsymbol{\Xi}\). This relationship is vital for the effective execution of computations.  
Figure 2: Timing characteristics of nodes.  
Furthermore, to ensure that each resulting PE finishes its tasks at different times, certain constraint must be imposed on the projection vector. This constraint is outlined in [47] and are essential for optimizing the performance of the system. The details of this constraint is as follows:  
\[\mathbf{Y}\mathbf{T}\
eq\mathbf{0} \tag{15}\]  
Considering the limitations placed on \(\mathbf{\Gamma}\), Equation (15), and using the scheduling vector \(\mathbf{Y}=[1\;\;0]\), the projection vector that leads to the hybrid systolic design can be expressed as follows:  
\[\mathbf{\Gamma}=\;\;\;[\;1\;\;\;\;\;0\;] \tag{16}\]  
Since \(\mathbf{\Gamma}\) represents the null space of the projection matrix \(\Xi\), The projection matrix can be formulated in the following way.  
\[\Xi=\;\;\;[\;0\;\;\;\;\;1\;] \tag{17}\]  
This formulation is essential for understanding how the projection vector influences the overall system's behavior. By employing the properties of the null space, we can derive a clearer representation of the projection matrix, which is critical for the subsequent analysis and implementation of the hybrid systolic design.  
By substituting the obtained projection vector \(\Xi=[0\;\;1]\) into Equation (14), we can derive the related projection function. The formulation of the projection function provides insights into the behavior of the system under the specified constraints, enabling us to express it as follows:  
\[\overline{\mathbf{\Gamma}}(\mathbf{\Gamma})=j \tag{18}\]  
This implies that the nodes of the dependency graph (DG) can be aligned along the \(j\) direction, allowing for computation through a one-dimensional systolic array. This configuration simplifies the architecture, making it well-suited for applications in resource-constrained environments.
# Three-Dimensional Multi-Agent Foraging Strategy Based on Local Interaction
## 5 3D Multi-Agent Foraging Strategy
### Provably Complete Puck Search
#### 5.1.1 Distributed Generation of a Spanning Tree  
As the first step in solving the above problem, we introduce a distributed Breadth First Search (BFS) algorithm, inspired by [41]. In [41], a distributed algorithm introduced to ensure that a node in the network can guide a moving object across the network to the goal. Algorithm 2 in [41] can be applied to make a spanning tree \(T\) rooted at an agent.  
The goal sensor in Algorithm 2 of [41] represents the root in Algorithm 1. In Algorithm 1, each agent \(n\) stores and updates \(hops_{g}(n)\), which indicates the hop distance to the root, say, \(n_{r}\). The root \(n_{r}\) initializes \(hops_{g}(n_{r})=0\). Initially, every other agent initializes \(hops_{g}(n)=\infty\), where \(n\
eq n_{r}\). In Algorithm 1, \(ancestor(n)\) denotes the set of ancestors of \(n\).  
```
1: Every agent \(n\) contains \(hops_{g}(n)\), which indicates the hop distance to the root;
2: Every agent \(n\) contains \(ancestor(n)=\{\}\);
3: The root \(n_{r}\) sets \(hops_{g}(n_{r})=0\) initially;
4: One initially sets \(hops_{g}(n)=\infty\) where \(n\
eq n_{r}\);
5: Initially, \(n_{r}\) transmits \(ancestor(n_{r})\) and \(hops_{g}(n_{r})\) to its neighbors;
6:repeat
7:\(n\leftarrow\) every agent;
8:if the agent \(n\) receives \(ancestor(m)\) and \(hops_{g}(m)\) from its neighbor, say \(m\in\mathcal{N}(n)\)then
9: The agent \(n\) updates its hop distance information using (6);
10:if\(hops_{g}(n)\) becomes \(hops_{g}(m)+1\) under (6) then
11: The agent \(n\) updates \(ancestor(n)\) using (7);
12: The agent \(n\) broadcasts \(hops_{g}(n)\) and \(ancestor(n)\) to its neighbors;
13:endif
14:endif
15:until\(hops_{g}(n)\
eq\infty\) for all \(n\);
```  
**Algorithm 1** Distributed BFS algorithm to generate a spanning tree \(T\)  
Initially, the root \(n_{r}\) sends its hop distance information \(hops_{g}(n_{r})\) and \(ancestor(n_{r})\) to its neighbors. Suppose that an agent \(n\) receives a hop distance message from its neighbor, say, \(m\in\mathcal{N}(n)\). Then, \(n\) updates its hop distance information under  
\[hops_{g}(n)=min(hops_{g}(m)+1,hops_{g}(n)). \tag{6}\]  
When \(hops_{g}(n)\) becomes \(hops_{g}(m)+1\) under (6), the ancestor set (\(ancestor(n)\)) of \(n\) is updated:  
\[ancestor(n)=\{ancestor(m),m\}. \tag{7}\]This implies that the set \(\mathit{ancestor}(n)\) is generated by adding \(m\) to the set \(\mathit{ancestor}(m)\). When \(\mathit{hops}_{g}(n)\) becomes \(\mathit{hops}_{g}(m)+1\) under (6), \(n\) broadcasts the updated \(\mathit{hops}_{g}(n)\) and \(\mathit{ancestor}(n)\) to its neighbors.  
The tree \(T\) is built until \(\mathit{hops}_{g}(n)\
eq\infty\) for all \(n\). In [41; 42], it was proved that the number of message broadcasts of each agent in this algorithm is one. This implies that Algorithm 1 has a computational complexity of \(O(1)\).
## 2 Materials and Methods
### Data
#### 2.1.2 Ground-Based Observations  
Visibility meters estimate visibility (in meters) by measuring the intensity of light scattered and absorbed as a laser beam passes through the atmosphere. Each minute, the instrument produces a visibility value based on multiple measurements sampled at 15 to 30 s intervals. The 10 min moving average of these values is used as the current visibility reading [32].  
Ground-based visibility data were obtained from 176 stations selected for their relatively high data quality, as identified by the National Meteorological Satellite Center (NMSC). Based on the land-sea mask (lsmask) in the GK2A fog detection product, 124 sta  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **GK2A Fog Product** & **Remarks** \\ \hline
1 & Clear sky & - \\
2 & Middle/high cloud & - \\
3 & Unknown & Partially satisfy the fog criteria \\
4 & Probably fog & - \\
5 & Fog & - \\
6 & Snow & - \\
7 & Desert or semi-desert & Prefixed by land use \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of GK2A fog detection product.  
tions were categorized as inland and 52 as coastal, including island stations (Figure 1). A fog event was defined when the observed visibility was less than 1 km, and the RH measured by co-located ASOS or AWS instruments was equal to or greater than 88% [30].  
For the case studies, ground-based observations from ASOS were used, including surface air temperature, RH, and wind speed. The vertical structure and depth of fog were investigated by utilizing low-level cloud base height derived from ceilometers and cloud top height data from GK2A.
## 3 Finite Element Analysis
### Comparison between Experimental Investigations and FEA  
Finite element modal analysis is conducted on the experimental specimen. The sections and material properties are same as the test specimen. The damping percentage is 0.05 according to Code for Seismic Design of Buildings (GB50011-2001) of China. The first five order frequencies are calculated, which agree well with experimental results as shown in Table 4. Comparison between finite element result and test result indicates the accuracy of the finite element modal analysis.
# An Integrated Access and Backhaul Approach to Sustainable Dense Small Cell Network Planning
###### Abstract  
Integrated access and backhaul (IAB) networks offer transformative benefits, primarily their deployment flexibility in locations where fixed backhaul faces logistical or financial challenges. This flexibility is further enhanced by IAB's inherent ability for adaptive network expansion. However, existing IAB network planning models, which are grounded in the facility location problem and are predominantly addressed through linear programming, tend to neglect crucial geographical constraints. These constraints arise from the specific deployment constraints related to the positioning of IAB donors to the core network, and the geographic specificity required for IAB-node placements. These aspects expose an evident research void. To bridge this, our research introduces a geographically aware optimization methodology tailored for IAB deployments. In this paper, we detail strategies for both single-hop and multi-hop situations, concentrating on IAB donors distribution and geographical constraints. Uniquely in this study, we employ the inherent data rate limitations of network nodes to determine the maximum feasible hops, differing from traditional fixed maximum hop count methods. We devise two optimization schemes for single-hop and multi-hop settings and introduce a greedy algorithm to effectively address the non-convex multi-hop challenge. Extensive simulations across various conditions (such as diverse donor numbers and node separations) were undertaken, with the outcomes assessed against the benchmark of the single-hop scenario's optimal solution. Our findings reveal that the introduced algorithm delivers efficient performance for geographically constrained network planning.  
60 GHz; mmWave; integrated access and backhaul; mix integer linear problem; location coverage problem; multi-hop; greedy algorithm +
Footnote †: journal: Information  
000
## 3 Results
### Response of Vegetation to Drought in Xinjiang
#### 3.3.2 Spatial Distribution of Vegetation Condition Response Times to Climate Change in Xinjiang
Additionally, regions with high drought tolerance (with a large SPEI12 response area) had far smaller SPEI24 response areas than those responding  
Figure 12: Spatial distribution of the SPEI time scales corresponding to the maximum correlation coefficients between vegetation conditions represented by NDVI (**a**) and SIF (**b**) and SPEI. (**a**) NDVI and (**b**) SIF are shown with SPEI03, SPEI06, SPEI12, and SPEI24, representing the dominant timescales of vegetation response to drought. This figure illustrates the temporal dynamics of vegetation response to drought stress. to SPEI12, suggesting that even highly drought-tolerant vegetation is affected by long-term interannual climate variability. Based on the analysis of vegetation conditions represented by SIF, the overall proportion of areas where SIF responded to SPEI03 was notably smaller than that of NDVI, probably due to the lack of valid SIF data in parts of southern Xinjiang. However, in ecological zones R1, R2, and R3, the response time of vegetation to climate change appeared to extend, particularly with a higher overall proportion of SIF responses to SPEI24 compared to NDVI. This suggests that vegetation exhibits stronger drought tolerance at the physiological level. The response time distribution of natural forests and grasslands to climate change was nearly uniform, with the largest area proportion associated with SPEI12, followed by SPEI24, and the smallest with SPEI06. Compared with NDVI, the proportion of SIF responses to SPEI03 was significantly reduced, shifting from the varied response times seen in NDVI to a more similar distribution for SIF. This indicates that while different vegetation types show temporal differences in surface responses to climate change, their physiological response times are relatively consistent. For example, although  
Figure 13: Area proportion of SPEI time scales corresponding to maximum correlation coefficients between vegetation indices and SPEI across different ecological regions and vegetation types. (**a**) displays NDVI correlations by region, whereas (**b**) shows SIF correlations by region. (**c,d**) present NDVI and SIF correlations, respectively, by vegetation type. natural forests structurally demonstrate a higher drought tolerance than natural grasslands, their physiological response times are nearly identical.
## 2 Mathematical Model
### Objective Function  
An MINLP model is proposed. The minimum investment is used as the objective function of the model, as shown in Equation (1).  
\[\text{Min}\ F=F_{PP}+F_{GP}+F_{S}+F_{C} \tag{1}\]  
where \(F\) is the pipeline network investment, CNY. \(F_{PP}\) is the production pipeline investment, CNY. \(F_{GP}\) is the gathering pipeline investment, CNY. \(F_{S}\) is the station investment, CNY. \(F_{C}\) is the plant investment, CNY.  
(1) Production pipeline investment.  
The investment in production pipelines is the cost of connecting all wells and stations in the network, as shown in Equation (2).  
\[F_{PP}=\sum_{i\in I}\sum_{j\in I}\delta_{ij}c_{ij}I_{ij} \tag{2}\]  
where \(\delta_{ij}\) is the connection decision variable between wells and stations, 0 or 1. \(c_{ij}\) is the price of the pipeline between wells and stations, CNY/m. \(L_{ij}\) is the pipeline length from wells to stations, m. \(L_{ij}=\left(\left(x_{i}-x_{j}\right)^{2}+\left(y_{i}-y_{j}\right)^{2}\right)^{ 0.5}\), \(\left(x_{i},y_{i}\right)\) is the well coordinate, and \(\left(x_{j},y_{j}\right)\) is the station coordinate.  
(2) Gathering pipeline investment.  
The investment in gathering pipelines is the cost of connecting the pipelines between all stations in the network, as shown in Equation (3).  
\[F_{GP}=\sum_{j\in I}\sum_{j\in I}\delta_{jj^{\prime}}c_{jj^{\prime}}L_{jj^{ \prime}} \tag{3}\]  
where \(\delta_{jj^{\prime}}\) is the connection decision variable between stations, 0 or 1. \(c_{jj^{\prime}}\) is the price of the pipeline between stations, CNY/m. \(L_{jj^{\prime}}\) is the pipeline length between stations, m. \(L_{jj^{\prime}}=\left(\left(x_{j}-x_{j^{\prime}}\right)^{2}+\left(y_{j}-y_{j^{ \prime}}\right)^{2}\right)^{0.5}\), \(\left(x_{j},y_{j}\right)\), and \(\left(x_{j^{\prime}},y_{j^{\prime}}\right)\) are the stations or plant coordinate.  
(3) Station investment.  
The station investment is the sum of the investment costs of all stations in the network, as shown in Equation (4).  
\[F_{S}=\sum_{j\in I}\delta_{j}f_{S} \tag{4}\]  
where \(\delta_{j}\) is the station location decision variable, 0 or 1. \(f_{S}\) is the cost of the station, CNY.  
(4) Plant investment.  
The plant investment is the sum of the investment costs of all plants in the pipeline network, as shown in Equation (5).  
\[F_{C}=\sum_{k\in K}\delta_{k}f_{P} \tag{5}\]  
where \(\delta_{k}\) is the plant location decision variable, 0 or 1. \(f_{P}\) is the cost of plant, CNY.
## 3 The Results
### Physiological Parameter and Biochemical Indexes in Blood  
The values of Hb, RBC, MCH, PCV, and MCV in gazelles from the affected pasture were significantly lower (\(p\) < 0.01) than those in unaffected gazelles (Table 3).  
The levels of TP, ALB and GLB in blood were significantly lower (\(p\) < 0.01) than those in unaffected gazelles, while levels of AST, LDH, CPK, and ALP in animal blood were extremely higher (\(p\) < 0.01) than those in unaffected gazelles (Table 4).  
The activities of T-AOC, SOD, GSH-Px, and CAT in animal blood from gazelles in the affected pasture were significantly lower (\(p\) < 0.01) than those in unaffected gazelles, while serum MDA was extremely higher (\(p\) < 0.01) than that in unaffected gazelles (Table 5).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Items** & **Affected Gazelles** & **Healthy Gazelles** \\ \hline AST (U/L) & 132.47 \(\pm\) 11.22 * & 89.98 \(\pm\) 8.37 \\ ALT (U/L) & 45.77 \(\pm\) 4.35 & 44.77 \(\pm\) 3.57 \\ LDH (U/L) & 553.48 \(\pm\) 34.58 * & 425.73 \(\pm\) 30.75 \\ CPK (U/L) & 386.37 \(\pm\) 63.86 * & 255.37 \(\pm\) 54.97 \\ ALP (U/L) & 1223.76 \(\pm\) 207.83 * & 751.28 \(\pm\) 93.56 \\ CR (umol./L) & 65.56 \(\pm\) 6.67 & 66.74 \(\pm\) 5.27 \\ TP (g/L) & 38.63 \(\pm\) 4.48 * & 56.38 \(\pm\) 3.86 \\ ALB (g/L) & 24.86 \(\pm\) 3.58 * & 32.86 \(\pm\) 2.36 \\ GLB (g/L) & 13.86 \(\pm\) 2.49 * & 24.86 \(\pm\) 2.88 \\ A/G & 1.84 \(\pm\) 0.21 * & 1.29 \(\pm\) 0.14 \\ Chol (mmol/L) & 1.97 \(\pm\) 0.21 & 1.89 \(\pm\) 0.23 \\ \hline \hline \end{tabular}  
* Indicates great difference at \(p\) < 0.01.  
\end{table}
Table 4: Physiological and biochemical levels in blood in gazelles.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Elements** & \multicolumn{2}{c}{**Blood**} & \multicolumn{2}{c}{**Hair**} \\ \cline{2-4}  & **Affected** & **Unaffected** & **Affected** & **Unaffected** \\ \hline Cu & 0.18 \(\pm\) 0.03 * & 1.61 \(\pm\) 0.63 & 3.99 \(\pm\) 0.33 * & 7.29 \(\pm\) 0.37 \\ Mo & 0.39 \(\pm\) 0.03 * & 0.23 \(\pm\) 0.02 & 4.35 \(\pm\) 0.06 * & 2.49 \(\pm\) 0.04 \\ Fe & 442.67 \(\pm\) 62.38 & 439.65 \(\pm\) 63.47 & 318.65 \(\pm\) 43.26 & 316.75 \(\pm\) 45.64 \\ Mn & 0.53 \(\pm\) 0.02 & 0.52 \(\pm\) 0.03 & 11.65 \(\pm\) 1.58 & 11.87 \(\pm\) 1.86 \\ Co & 0.55 \(\pm\) 0.03 & 0.54 \(\pm\) 0.05 & 1.03 \(\pm\) 0.04 & 1.02 \(\pm\) 0.03 \\ \hline \hline \end{tabular}  
* Indicates great difference at \(p\) < 0.01.  
\end{table}
Table 2: The mineral element in blood and hair in gazelles (\(\mu\)g/g).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Affected Gazelles** & **Healthy Gazelles** \\ \hline Hb (g/L) & 90.76 \(\pm\) 7.41 * & 119.63 \(\pm\) 8.87 \\ RBC (10\({}^{12}\)/L) & 5.73 \(\pm\) 0.33 * & 7.99 \(\pm\) 0.25 \\ PCV (\%) & 39.63 \(\pm\) 3.76 * & 53.63 \(\pm\) 3.58 \\ MCV (fL) & 47.35 \(\pm\) 3.21 * & 53.47 \(\pm\) 3.76 \\ MCH (pg) & 10.74 \(\pm\) 1.64 * & 16.38 \(\pm\) 1.56 \\ MCHC (\%) & 22.76 \(\pm\) 1.37 & 24.31 \(\pm\) 1.57 \\ WBC (10\({}^{9}\)/L) & 8.48 \(\pm\) 0.58 & 9.76 \(\pm\) 0.52 \\ \hline \hline \end{tabular}  
* Indicates great difference at \(p\) < 0.01.  
\end{table}
Table 3: Blood hematological levels in gazelles.