esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Joint Spatial-Code Clustered IA Scheme
### Error Performance of the JSCC-IA Scheme with Different Propagation Delays  
Based on the analysis in Section 4.2, we progress to a more complex but practical situation where the users have different propagation delays. As shown in Figure 3, there might be two incomplete message frames during one message interval. Without loss of generality, the propagation delay between the \(k\)-th and \(i\)-th users is assumed to be the multiples of the duration of one PN chip, _i.e._,  
\[\tau_{ki}=\Delta_{ki}T_{c},\ \Delta_{ki}=0,1,...,L-1 \tag{54}\]  
Then the reconstructed signal waveform vector at the \(k\)-th receiver can be expressed as:  
\[\begin{split}\mathbf{\tilde{s}}_{k}(t)&=\gamma_{ik} \mathrm{U}_{k}^{\mathrm{H}}\mathbf{H}_{ik}\mathbf{V}_{i}\mathbf{m}_{k}(0)g_{k} (t)\\ &+\sum_{i=1,j\
eq k}^{K}\{\gamma_{ki}\mathrm{U}_{k}^{\mathrm{H}} \mathbf{H}_{ik}\mathrm{V}_{i}[\mathbf{m}_{i}(0)g_{i}(t-\tau_{ki})+\mathbf{m}_ {i}(-1)g_{i}(t+T_{b}-\tau_{ki})]\}\\ &+\mathbf{U}_{k}^{\mathrm{H}}\mathbf{n}_{k}(t),\ 0\leq t\leq T_{b}\end{split} \tag{55}\]  
The demodulator employ \(g_{k}(t)\) to correlate with \(\mathbf{\tilde{s}}_{k}(t)\), and the test statistic vector can be written as:  
\[\begin{split}\mathbf{\hat{s}}_{k}&=\int_{0}^{T_{b }}\mathbf{\tilde{s}}_{k}(t)g_{k}(t)dt\\ &=\gamma_{ik}\mathrm{U}_{k}^{\mathrm{H}}\mathbf{H}_{ik}\mathbf{V} _{k}\mathbf{m}_{k}(0)\int_{0}^{T_{b}}g_{k}(t)g_{k}(t)dt\\ &+\sum_{i=1,j\
eq k}^{K}\left\{\gamma_{ki}\mathrm{U}_{k}^{ \mathrm{H}}\mathbf{H}_{ik}\mathrm{V}_{i}\bigg{[}\mathbf{m}_{i}(0)\int_{0}^{T_{b }}g_{k}(t)g_{i}(t-\tau_{ki})dt+\mathbf{m}_{i}(-1)\int_{0}^{T_{b}}g_{k}(t)g_{i}( t+T_{b}-\tau_{ki})dt\right]\right\}\\ &+\int_{0}^{T_{b}}\mathrm{U}_{k}^{\mathrm{H}}\mathbf{n}_{k}(t)g_ {k}(t)dt\end{split} \tag{56}\]  
From Equations (24), (26), (28) and (54), the integrals in Equation (56) can be rewritten as:  
\[\int_{0}^{T_{b}}g_{k}(t)g_{i}(t-\tau_{ki})dt=E_{b}\rho_{\underline{k}i}(\Delta _{ki})/\,L \tag{57}\]  
\[\int_{0}^{T_{b}}g_{k}(t)g_{i}(t+T_{b}-\tau_{ki})dt=E_{b}\rho_{\underline{k}i}( \Delta_{ki}-L)/\,L \tag{58}\]  
Substitute Equations (57) and (58) into Equation (56), and the test statistic vector can be rewritten as:  
\[\mathbf{\hat{s}}_{k}=E_{b}\gamma_{ik}\mathbf{U}_{k}^{\mathrm{H}}\mathbf{H}_{ ik}\mathbf{V}_{k}\mathbf{m}_{k}(0)+\frac{E_{b}}{L}\sum_{i=1,j\
eq k}^{K}\left\{ \gamma_{ki}\mathrm{U}_{k}^{\mathrm{H}}\mathbf{H}_{ik}\mathrm{V}_{i}\Big{[} \mathbf{m}_{i}(0)\rho_{\underline{k}i}(\Delta_{ki})+\mathbf{m}_{i}(-1)\rho_{ \underline{k}i}(\Delta_{ki}-L)\Big{]}\right\}+\mathbf{\hat{n}}_{k} \tag{59}\]  
As shown in Equation (59), the desired signal vector \(\mathbf{m}(0)\) is degraded by the two consecutive messages from the other users, and the received SINR of the \(l\)-th data stream of the \(k\)-th user can be calculated as:\[\text{SINR}_{\text{JSCC2}}(k,l)=\frac{\text{P}_{\text{JSCC2}}(k,l)}{\text{I}_{ \text{JSCC2}}(k,l)+N_{0}E_{b}\ /\ 2} \tag{60}\]  
where:  
\[\text{P}_{\text{JSCC2}}(k,l)=\left|E_{b}\gamma_{k\text{i}b}\rho_{ \underline{k}\underline{l}}(0)\text{U}_{k,l}^{\text{H}}\text{H}_{\text{i}k} \text{V}_{k,l}m_{k,j}\ /\ L\right|^{2}\]
## 5 Simulation Results and Analysis  
### Parameter Settings  
The simulation has been conducted using Python 3.6 and MATLAB 2023b, based on modifications and simulations built upon the code provided in [34]. The simulation scenario involves a two-way highway covered by the RSU communication range, where randomly distributed vehicles travel at constant speeds in their respective lanes and use NR-V2X Side-Link technology for V2V communication. The length of the highway, \(D\), is 500 m, the RSU coverage range, \(D_{RSU}\), is 250 m, and the maximum distance, \(w\), between vehicles and receivers is 150 m. All vehicles utilize four different priority queues of length \(L\). And the vehicles occupy a channel bandwidth of 10 MHz within the 5.9 GHz frequency band. The receiver has a noise figure of 9 dB. The path loss model features a standard deviation of 3 dB and a decorrelation distance of 25 m. Thus, the \(RSRP_{th}\) is -126 dBm.  
MPDQN employs a neural network with one hidden layer and updates its parameters using the Adam optimization method with learning rates \(lr_{Q}=5~{}\times~{}10^{-4}\) and \(lr_{x}=10^{-4}\). The experience replay buffer size \(M\) is set to 2000, and the sample size \(B\) is 128 [35]. Ornstein-Uhlenbeck noise is used as the exploration noise for the network, with a decay rate set to 0.15 and variance set to 0.0001. The key simulation parameters have been listed in Table 2.
###### Abstract  
Among the geographic elements, shape recognition and classification is one of the im portant elements of map cartographic generalization, and the shape classification of an areal settlement is an important part of geospatial vector data. However, there is currently no relatively simple and efficient way to achieve areal settlement classification. Therefore, we combined the skeleton line vector data of an areal settlement and the graph convolutional neural network to propose an areal settlement shape classification method that (1) extracts the skeleton line of the areal settlement to form a dual graph with nodes as edges, (2) extracts multiple features to obtain a graph representation of the shape, (3) extracts and aggregates the shape information represented by the areal settlement skeleton line using the graph convolutional neural network for multiple rounds to extract high-dimensional shape information, and (4) completes the shape classification of the high-dimensional shape information. The experiment used 240 samples, and the classification accuracy was 93.3%, with areal settlement shapes of E., F., and H-type achieving F-measures of 96.5%, 92.3%, and 100%, respectively. The result shows that the classification method of the areal settlement shape has high accuracy.  
aerial settlement; skeleton lines; graph convolutional neural network; shape classification; cartographic generalization +
Footnote †: journal: Applied Sciences
# Using Interval Analysis to Compute the Invariant Set of a Nonlinear Closed-Loop Control System
## 7 Conclusions and Outlook on Future Work  
In this paper, a method to compute the invariant set of a nonlinear system controlled by sliding mode approaches, which were designed in such a way that structural singularities were guaranteed to be prevented, was suggested. Numerically, the invariant set could be obtained using interval computation, if the controller was designed according to the equation:  
\[\dot{s}(\mathbf{x})=-s(\mathbf{x})\cdot p(\mathbf{x})\,\ \ p(\mathbf{x})>0. \tag{65}\]  
Avoiding singularities in \(u\) was ensured using a suitable Grobner basis representation. The various investigated examples showed a surface represented by a closed curve, a case where Bezout's identity had no solution and thus no controller can be found, and a surface that was a line. Additionally, a three-dimensional example was given and bounds, on the control input were considered.  
The presented interval technique for the identification of invariant sets can easily be generalized towards the computation of the region of attraction of asymptotically stable equilibria of nonlinear feedback control systems by verifying those domains in the state space in which the time derivative \(\dot{V}(\mathbf{x})\) of a Lyapunov function candidate was strictly non-positive within a level set characterized by \(V(\mathbf{x})=c>0\), where this curve needed to be proven by the suggested approach not to coincide with an invariant set. Straightforwardly, also generalizations to the identification of domains in the state space, for which the stability of stochastic differential equations cannot be verified, became possible. Such domains were derived in [48].  
In future work, the suggested procedure for the computation of invariant sets can be extended by the consideration of set valued uncertainty in the state variables included in the control signal \(u(\mathbf{x})\), which result either from an imperfect state measurement or from uncertainty if the state information included in the feedback control law is reconstructed by means of an interval observer [49]. Moreover, procedures for enhanced control parameterizations can be developed on the basis of the presented identification of invariant sets so that the volume of the intervals containing these sets is minimized. Hence, the aim will be to make the guaranteed provable stability domains as large as possible, as well as to maximize the regions of attraction of asymptotically stable equilibria by control re-parameterizations, to minimize the non-provable stability domains in the aforementioned stochastic settings, and to tune Lyapunov functions so that the provable stability domains show a minimum amount of pessimism.  
Figure 10: Input for random initial conditions.  
**Author Contributions:** The implementation and algorithm design were performed by [PERSON] under the supervision of [PERSON]. The paper was jointly written by [PERSON], [PERSON], and [PERSON].  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Numerical Fit Modeling for Temperature Mitigation in
## 1 Introduction
### Modeling Options
#### 1.1.1 Three-Dimensional Models  
Three-dimensional models can analyze heat exchange and air movements between multiple levels of air vertically. Generally, they can also handle horizontal movement of air. Ensemble forecasts (multiple runs with different inputs) are made to access variability.  
A three-dimensional model is necessary when synoptic air movement is significantly relevant. Several factors in evaluating the relevance of three-dimensional models to this project are discussed here.  
Generally, when heating rate differences exist, air movements increase, so it could be assumed that movement will increase between local SRM mitigation and non-mitigation target areas. However, calcite will reduce thermal radiation, which is a cause of instability,and the local SRM will be carried out over cities which are sources of local heating difference. This means that the urban heat island (UHI) temperature increases will be reduced, and any resulting air movement will be reduced. Therefore, the direct effect of local SRM is to decrease air movement.  
The goal of this paper is to focus on local effects due to local SRM. The material would be distributed widely and would have effects elsewhere. Dispersion is not a concern from a local modeling point of view because the operational plan is to continue to regenerate the particles as needed so that it remains constant. Therefore, for this reason large scale air movement is less relevant.  
Water clouds have sophisticated physics of condensation and evaporation that are three-dimensional. Three-dimensional models are useful when overturning movement in water clouds needs to be modeled. However, calcite will become widely and evenly dispersed as the distance from the source increases. The material will eventually be distributed evenly between the ground and the planetary boundary layer (PBL), which is the level of the atmosphere which is affected by disturbance or turbulence from the surface. Once the calcite is evenly distributed, movement of air would have no consequences on its concentration.  
There appears to be little need for 3D modeling. If the effects of the UHI are reversed by mitigation, then any effect that it had on synoptic weather patterns should return to those patterns before disruption by local heating.
# Tracking of Moving Targets Through Asynchronous Measures
## 4 Numerical Results  
We have simulated the scenario shown in Figure 1, which has been modeled as follows.  
1. A flying target moving on circular trajectories at fixed altitudes and tangential velocities.
2. A set of 6 anchors located at the edges of a regular hexagon of side \(d_{A}=2000\) m.
3. All the anchors are located at an altitude \(h_{A}=25\) m, similar to the common height of base stations.
4. At each step, the target moves and obtains a single ranging measure from one of the anchors. In other words, the measures are arriving serially at the target from the anchors, so the UAV occupies different positions in the trajectory. The target performs its localization and tracking by processing the ranging measures as they become available.
5. The ranging error is modeled as Gaussian with zero mean.
6. Each anchor has a certain probability of being NLoS (Non-Line-of-Sight) at each step, which prevents the corresponding range from being measured correctly.
7. The target is a priori aware of the LoS availability of each anchor. Therefore, it excludes the ranging measures that are affected by an NLoS link.  
We compared the results obtained through the proposed algorithm and the benchmarks, i.e., the commonly used EKF and PF. The simulations were performed at different values of target altitude, velocity and Root Mean Square Error (RMSE) of the ranging measures. As depicted in Figures 4-6, we can observe that the proposed algorithm performs better than the EKF and the PF. We can also notice a remarkable difference in the accuracy on the vertical (V) and horizontal (H) components, due to the fact that the target flies relatively close to the horizontal plane spanned by the anchors and thus all the ranging anchors tend to be quasi-horizontal, making the estimate of the horizontal position much more accurate than the vertical one. The results are further commented on in the following subsections.
## 3 Methods
### Extracting Feature Data
#### 3.2.3 Data Classification  
Based on the spacecraft mission log, we can categorize the obtained historical telemetry data of the spacecraft into several datasets under different working conditions. For each category of telemetry data under these conditions, we employ the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm to cluster them, outlining patterns or trends in the data. The distribution of data points identified by the clustering algorithm represents the \"features\" of specific operating conditions.  
As shown in the Figure 7, we first partition the data based on task time labels and then cluster the partitioned data. The data of a particular partition are divided into six clusters, and the data points from each cluster serve as a reference data group. The black data points are outliers and are removed during the feature extraction process.
## 5 Limitations  
While our proposed method demonstrates significant advantages in terms of simplicity, effectiveness, and efficiency, it is essential to acknowledge the limitations of our research.  
One limitation is that, although we have evaluated our method on a diverse set of harmful questions, further investigation is necessary to verify its performance on an even wider range of problematic content. Additionally, the prompts used for ADVER-SARIALREPHRASING in our method were created empirically, and optimizing these prompts could potentially enhance the attack performance, but this remains an area for future exploration.  
As new LLMs continue to emerge, it is crucial to examine the effectiveness of our method on these models to ensure its robustness and generalizability. Another aspect that merits further investigation is the relationship between prompt length and attack success rate, as our method generates shorter jailbreak prompts than existing techniques do.  
Our current method relies on the changes produced by the LLM during the iterative rewriting process, leveraging the model's ability to generate meaningful and coherent variations of the prompt. However, exploring more advanced prompt optimization techniques (e.g., reinforcement learning [30] and evolutionary algorithms [31]) could potentially guide the rewriting process toward generating more effective jailbreak prompts.  
Figure 4: Attack success rate (ASR; %) for the proposed method (ours), PAIR, and manual jailbreak attack (MJA) with and without the self-reminder defense. Baseline ASR with defense is indicated by red dashed line.  
As LLMs and defense strategies evolve, it is essential to continuously assess and adapt our method to stay ahead of potential countermeasures. Addressing these limitations and exploring these areas for improvement will be critical in advancing the field of jailbreak attack research.
# A New Bloom Filter Architecture
## 3 Proposed Algorithm
### 2-Phase Bloom Filter Architecture
#### 3.2.3 Update  
The functional Bloom filters used in our proposed architecture can provide incremental insertions and deletions by ensuring that each cell is programmed by a single key. When programming the return value for a given key, if a cell indicated by the hash index generated for the given key already has a value (meaning that the cell has already been programmed by another element), the cell is set to _conflict_**X**. Since each cell with a value other than **X** is programmed by a single key, the functional Bloom filter can provide delete operations. In deleting an element from a functional Bloom filter, each cell among the \(k\) cells with a value other than **X** is changed to \(0\); conflict cells are not changed in this process.  
Repeated insertions and deletions will increase the number of conflict cells, and, accordingly, degrade the performance of the functional Bloom filter. Hence, the proposed architecture should be reconstructed if the number of conflict cells is larger than a pre-defined threshold value. Determining the threshold value for the desired performance in terms of false positive rate and _indeterminable_ rate is beyond the scope of this paper.
# Secondary Atomization of a Biodiesel Micro-Emulsion Fuel Droplet Colliding with a Heated Wall
## 4 Conclusions  
The experiments have established that the regimes and outcomes of the interaction between diesel or biodiesel fuel micro-emulsion droplets and a heated wall have critical differences from the corresponding parameters known for other liquids. For example, by adding up to 50% of vegetable additive to the carbohydrate component of a micro-emulsion and decreasing the emulsifier concentration down to 10.45 vol. %, one can achieve a corona splash drop disintegration regime. Two- and three-dimensional plots were built for the main parameters of the secondary atomization of liquid droplets (\(d_{s}\)/\(D_{0}\), \(S_{1}\)/\(S_{0}\)) colliding with a heated wall for various water concentrations in the micro-emulsion (8-16 vol. %), droplet heating temperatures (300-500 \({}^{\circ}\)C), dimensions (1.8-2.8 mm), velocities (3-4 m/s) and emulsifier concentrations (10.45 vol. % and 20 vol. %). The rheological properties of the examined compositions were measured and accounted for when analyzing the experimental results. For prospective micro-emulsion compositions, it is possible to achieve \(S_{1}\)/\(S_{0}\) = 19 (maximum for the 3D recording) and \(d_{s}\)/\(D_{0}\) < 0.1. We established the determinative effect of the substrate temperature and water proportion in the micro-emulsion composition on the droplet-wall interaction regime and outcome. The temperature growth in the range from 300 \({}^{\circ}\)C to 500 \({}^{\circ}\)C leads to droplet breakup scenarios in different heat exchange regimes. This in general promotes a distinct variation in the integral quantitative characteristics of hydrodynamically disintegrating biodiesel micro-emulsion fuel droplets. Increasing the water proportion from 8 vol. % up to 16 vol. % changes the mean diameter of a child droplet at 300-500 \({}^{\circ}\)C by more than 50%. In this case, the overall surface area of the child droplets also increases more than tenfold. When comparing the integral characteristics of hydrodynamically disintegrating biodiesel micro-emulsion fuel droplets obtained via 2D and 3D recording, the latter method gave on average 24.5% lower \(d_{s}\)/\(D_{0}\) and 28.5% higher \(S_{1}\)/\(S_{0}\). With this in mind, 2D recording technique can be used for the experiments as a simpler and less laborious method, but with the introduction of an adjustment coefficient to account for the dimensional distribution of the generated child droplets.  
Investigation, A.E.A. and N.A.K.; Writing--original draft, M.V.P.; Writing--review & editing, V.A.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [Russian Science Foundation] grant number [18-73-00083].  
The authors declare no conflict of interest.
## 2 Materials and Methods  
### Data Availability  
Single-cell RNA-seq data deposited in the National Center for Biotechnology Information (NCBI) GEO database (Accession number: GSE75688), was used for data analysis. RNA-seq data of 94 single-cell samples from one double-positive (ER\({}^{+}\)/HER\({}^{2+}\)) breast cancer patient were used for this analysis. Illumina sequencing platform (HiSeq 2000, San Diego, CA, USA) was used to sequence cDNA libraries of 56 single-cell metastatic and 38 single-cell non-metastatic samples. Quality check was performed by fastQC (v.1.1.0).
# Eco-Efficient Cement-Based Materials Using Biomass Bottom Ash: A Review
## 6 Conclusions  
Based on the literature reviewed in this work, the feasibility of using BBA from different origins and with different treatments has been analyzed for application in cement-based materials such as mortars, concrete, or CTGM. It has been demonstrated that the characteristics of materials manufactured with BBA have improved and worsened, due to the variability in the properties of the ash and the different processes of manufacture used by each author.  
The following conclusions are obtained from the analysis of the literature:  
The results from mortar manufactured with BBA indicate that:  
The main components of the BBA are SiO2 and CaO, these values are appropriate to produce pozzolanic reactions and have a positive cementitious capacity.  
The use of BBA in the manufacture of mortar increases porosity, as a consequence of porosity, decreases density and lowers mechanical strength. The different studies agree that the negative effect on mechanical properties is due to the content of organic matter present in BBA.  
The results from concrete manufactured with BBA indicate that:  
Previous research reports that concrete made with BBA present worse mechanical behavior than control samples, although the values are enough to ensure the minimum requirements established by the different standards.  
Concrete with lower water to binder ratio are the type most affected by the BBA incorporation, due to the high water absorption which reduces the workability and decrease the mechanical behavior.  
BBA with high alkali elements contents such as K2O has no negative effects on the durability properties of concrete.  
The results of the cement treatment granular material manufactured with BBA indicate that:  
BBA with high CaO contents has stabilizing properties to use in expansive soils, improving the bearing capacity of the soils.  
BBA with high Si + Fe + Al and CaO content with cement improve the mechanical behavior of the treated soil.  
The compaction process and moisture content determine the development of compressive strength of soils treated with BBA.  
The real possibility of using BBA in cement-based materials for its application in civil engineering has been demonstrated, promoting a circular economy model in which waste from the generation of energy from biomass is used reducing or eliminating its deposit in landfills.