esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Numerical Results
Again, IDRR outperforms IDRRP, while the latter does so with DRR. Moreover, in this case, denser discretizations for DRR perform slightly worse than in the benchmark problem, as it stems immediately from the comparison of Figures 9 and 11 or Tables 1 and 6. The behavior of the throughput against the control period for 1 to 4 AGVs and \(T\in\{1,0.5,0.2,0.1,0.05,0.02\}\) [s] is collected in Table 7. Once again, significant improvements are shown in all cases as \(T\) decreases. The improvement percentages with respect to \(T=1\) s, displayed in Figure 12, lie within the interval \([4.4,34.3]\%\), while for the benchmark problem, it was \([0.4,13.5]\%\) (recall Figure 10). Finally, the waiting times, traveled distances, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**AGVs** & **T** & **IDRR** & **IDRRP** & **DRR** & **DRR-2m** & **DRR-1m** \\ \hline
1 & 1 & 0.940 & 0.931 & 0.930 & 0.885 & 0.854 \\  & 0.5 & 0.970 & 0.961 & 0.961 & 0.945 & 0.939 \\  & 0.2 & 0.974 & 0.965 & 0.965 & 0.962 & 0.959 \\  & 0.1 & 0.989 & 0.980 & 0.980 & 0.979 & 0.978 \\  & 0.05 & 0.995 & 0.986 & 0.986 & 0.986 & 0.985 \\  & 0.02 & 0.997 & 0.988 & 0.988 & 0.988 & 0.988 \\ \hline
2 & 1 & 0.869 & 0.868 & 0.863 & 0.835 & 0.815 \\  & 0.5 & 0.931 & 0.930 & 0.904 & 0.899 & 0.898 \\  & 0.2 & 0.961 & 0.959 & 0.919 & 0.915 & 0.914 \\  & 0.1 & 0.967 & 0.966 & 0.923 & 0.921 & 0.920 \\  & 0.05 & 0.969 & 0.969 & 0.925 & 0.923 & 0.923 \\  & 0.02 & 0.970 & 0.970 & 0.925 & 0.925 & 0.925 \\ \hline
3 & 1 & 0.845 & 0.844 & 0.710 & 0.676 & 0.659 \\  & 0.5 & 0.904 & 0.902 & 0.716 & 0.707 & 0.704 \\  & 0.2 & 0.938 & 0.934 & 0.721 & 0.714 & 0.711 \\  & 0.1 & 0.941 & 0.938 & 0.724 & 0.722 & 0.721 \\  & 0.05 & 0.944 & 0.942 & 0.725 & 0.724 & 0.723 \\  & 0.02 & 0.946 & 0.945 & 0.726 & 0.725 & 0.725 \\ \hline
4 & 1 & 0.809 & 0.806 & 0.525 & 0.495 & 0.482 \\  & 0.5 & 0.867 & 0.863 & 0.527 & 0.516 & 0.513 \\  & 0.2 & 0.896 & 0.896 & 0.531 & 0.522 & 0.520 \\  & 0.1 & 0.902 & 0.898 & 0.532 & 0.529 & 0.527 \\  & 0.05 & 0.905 & 0.902 & 0.535 & 0.532 & 0.531 \\  & 0.02 & 0.908 & 0.905 & 0.536 & 0.535 & 0.535 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Benchmark layout—Test 2: Average velocities (in [m/s]) for different \(T\) and 1 to 4 AGVs.
# Challenges and Recommendations for Improved Identification of Low ILUC-Risk Agricultural Biomass
#### 1.1.2 Results-Based Crediting  
The focus of this study is the final stage of the low ILUC-risk additionally screening discussed in Section 1.1.1; i.e., the highlighted box in Figure 1, which is where applicants for low ILUC-risk certification (more specifically, those undertaking \"yield improvement\"-type projects on existing agricultural land; see points (i) and (ii) in the list above) must construct a yield baseline from their historical yield data.  
The method of comparing a farm's actual yields with a pre-determined baseline makes the low ILUC-risk system an example of \"results-based\" crediting, where incentives are coupled to demonstrated outcomes; here, the outcome is the amount of above-baseline feedstock production. Results-based crediting can be contrasted with \"practice-based\" crediting, where incentives are given simply for adopting practices which are likely to be beneficial. An example would be the greening payments of the EU's Common Agricultural Policy (CAP).  
A results-based low ILUC-risk scheme offers some certainty to buyers and society at large that a given batch of biofuel feedstock has avoided stimulating agricultural expansion, and hence comes with environmental benefits compared with a non-certified  
Figure 1: Sketch of the key steps for low ILUC-risk certification applicants; the focus of this paper is highlighted.  
batch. However, the results-based framing is less attractive to producers, who in the face of uncontrollable weather conditions can predict neither the success of their additionally measure nor their future production. There is also a danger that a purely results-based scheme, when combined with variability over time and across geographies, may end up certifying business-as-usual production if temporary fluctuations are able to masquerade as yield improvements according to the comparison baseline (this is known as a \"free-riding problem\").  
In light of these issues, hybrid schemes which combine elements of both results- and practice-based approaches are sometimes appropriate [7]. Indeed, the low ILUC-risk methodology goes some way to incorporating practice-based conditions, as farmers must demonstrate adoption of an additionally measure, and their anticipated yield increase must be plausibly connected to this measure. This mitigates free riding to some extent (to be further explored in this study), but it still leaves producers to shoulder the uncertainty of weather variability.
# Classification of Speed Change and Unstable Flow Segments Using Geohash-Encoded Vehicle Big Data
## 3 Methodology  
### Determination of Unstable Flow  
SMS and TMS are equal if all vehicles in the same space have the same speed. However, divergent individual vehicle speeds introduce discernible differentials between these two means, as illustrated in Figure 1. The relationship between these mean speeds is mathematically expressed in Equation (1). Employing the ratio of the square of TMS speed's standard deviation to TMS itself offers an effective measure to quantify the dissimilarity between these mean speeds. This quantification is particularly informative when speed variations are uniformly spread across a designated space. In such instances, the extent of speed variability among vehicles may fluctuate, as dictated by the variance denoted as \(\sigma^{\prime}\). Notably, the range of Speed Fluctuation (SF) per speed resides within the confines defined by the function of the two mean speeds, as described by Equation (2). This depiction is graphically elucidated in Figure 2.
## 4 Results
### Notification Experience Comparison
#### 4.2.3 Importance Versus Frequency  
The third and final comparison relates to the perceived importance versus frequency (see Figure 7). In this comparison, we observed that the Calm--Reactive setting falls on the lower end of the perceived notification frequency spectrum, while its perceived importance is situated in a neutral position. Similarly, the Calm--Proactive setting exhibits a similar characteristic, but with a slightly higher notification frequency. For the Expected and Proactive characteristics, we found that they have a neutral stance in terms of the perceived frequency and importance of notifications. Overwhelming--Proactive also displays similar traits. However, the Unexpected--Reactive characteristic stands out as it indicates a higher importance among all the other characteristics. This indicates that the notifications received have a higher importance rating and are of moderate frequency. This may be due to the nature of unexpected notifications, as they require greater attention and engagement since they are considered important yet occur unexpectedly.  
The chart delineates user preferences for Calm--Proactive notifications, emphasizing the significance of managing notification frequency and ensuring a positive user experience. Conversely, the Overwhelming--Reactive notifications underscore the need to minimize such experiences due to their negative impact. The presence of N/A responses raises questions about the completeness of the qualitative characteristics or may indicate the existence of unique user experiences not captured by the predominant characteristics. This underscores the importance of a nuanced approach to understanding user interactions with notifications, taking into account the varied nature of user responses.  
Figure 7: Comparison of notification importance versus perceived notification frequency.
## 7 Discussion
In adaptive jitter, pre-forwarding delays depend on the quality of the link through which the packet was received. Not surprisingly, the performance is then better than in static jitter configurations, both in terms of control overhead and route quality. Under the shortest-path mode, route quality is similar for all settings, but adaptive jitter reduces the number of RREQs and RREPs up to \(30\%\). There is still a trade-off between route quality and flooding delay, meaning that adaptive jitter entails additional delays; such a delay becomes negligible as the network density grows.
# Single-Shot Compressed Imaging via Random Phase Modulation
## 2 Fundamentals of Compressed Sensing
### Measurement Matrix  
Different from the Shannon sampling scheme, in compressed sensing, the subsampling measurements can be obtained via linear and incoherent projections corrupted by noise as follows  
\[\mathbf{y} =\mathbf{\Phi}\mathbf{x}+\mathbf{e}=\left(\begin{array}{cccc}\phi_{11}&\phi _{12}&\cdots&\phi_{1N}\\ \phi_{21}&\phi_{22}&\cdots&\phi_{2N}\\ \vdots&\vdots&\ddots&\vdots\\ \phi_{M1}&\phi_{M2}&\cdots&\phi_{MN}\end{array}\right)\left(\begin{array}{ c}x_{1}\\ x_{2}\\ \vdots\\ x_{N}\end{array}\right)+\left(\begin{array}{c}e_{1}\\ e_{2}\\ \vdots\\ e_{M}\end{array}\right) \tag{2}\] \[=\mathbf{\Phi}\mathbf{\
abla}\mathbf{\alpha}+\mathbf{e}=\mathbf{\Theta}\mathbf{\alpha}+ \mathbf{e}\]  
where \(\mathbf{\Phi}\in\mathbb{R}^{M\times N}\) is measurement matrix, \(\mathbf{\Theta}\) is the sensing matrix and \(\mathbf{e}\) is the random additive noise.  
Since the measurement process of CS is not adaptive, the measurement matrix is fixed and independent of the signal \(\mathbf{x}\), which means that the measurement matrix should be universal for any sparse or compressible signal. The effects of the measurement matrix are reflected in two aspects: On the one hand, a stable measurement matrix \(\mathbf{\Phi}\) can preserve salient information in any \(K\)-sparse or compressible signal in a compressed sampling process; On the other hand, the measurement matrix plays a crucial role in the reconstruction process, a 'good' measurement matrix can clearly enhance the recovery performance for recovering \(\mathbf{x}\) only from the \(M\) measurements \(\mathbf{y}\).  
In order to ensure the successful recovery of a sparse signal from fewer measurements, CS theory indicates that if the Restricted Isometry Property (RIP [8]) is satisfied, i.e., the measurement matrix satisfies the \(K\)-order RIP of the parameter \(\delta_{K}\in(0,1)\), if  
\[(1-\delta_{K})\|\mathbf{\alpha}\|_{2}^{2}\leq\|\mathbf{\Theta}\mathbf{\alpha}\|_{2}^{2} \leq(1+\delta_{K})\|\mathbf{\alpha}\|_{2}^{2} \tag{3}\]  
holds for all sparse vectors \(\mathbf{\alpha}\) having no more than \(K\) nonzero entries.
# Provenance and Paleo-Environment of the Late Carboniferous Bauxite Formations in Southern Shanxi
## 5 Discussion
### Source of Metallogenic Materials
The complete amalgamation between the North China Block and the Qinling Block occurred between the Early and Middle Triassic [56], during a time when the two blocks were separated by an ocean. Other scholars investigating the provenance of Carboniferous bauxite on the southern margin of North China contend that the bottom iron-rich clay rocks are primarily products of in situ weathering and karstification of carbonate rocks, whereas the bauxite and top clay rocks are allochthonous deposits [19]. As previously discussed, the lithologies of the metallogenic source rocks for the iron-rich clay rocks and bauxite are generally consistent, although the source of the top beds indicates slight deviations. This study posits that the bauxite and underlying iron-rich clay rocks may be linked to the weathering of Ordovician carbonates, further influenced by high-aluminum, high-iron materials resulting from the  
Figure 10: Diagram of the material sources of aluminiferous strata in Pinglu. Note: (a) TiO\({}_{2}\)-Al\({}_{2}\)O\({}_{3}\) lithology discrimination diagram, (b) Zr/Sc–Th/Sc diagram, (c) La/Sc–Th/Co diagram. weathering of terrestrial source rocks; conversely, the overlying clay rocks may be products transported from weathered source rocks.
# Swarm-Intelligence-Centric Routing Algorithm for Wireless Sensor Networks
## 3 Proposed SICROA  
Because all nodes in a WSN environment are mobile, the network topology changes dynamically over time; the data transmission radius is limited by the available battery power; and it has an unstable routing path, where link disconnections occur frequently owing to interference, multipath fading, and/or collisions. To overcome these difficulties, this paper presents a biomimetic algorithm based on the SI-centric routing algorithm (SICROA) for WSNs. The said method is capable of providing an agile and appropriate response to routing problems.
## 2 Materials and Methods
### Theory of Threat Modeling
#### 2.4.3. STRIDE  
The basis of threat modeling is a brainstorming methodology referred to as STRIDE. STRIDE is the abbreviation of the Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege. It was invented in 1999 and adopted by Microsoft (www.microsoft.com, accessed on 15 May 2022) in 2002 [24] and is used to identify threats for different threat categories. These threats categories are defined as follows [7]:  
* **S**poofing: Attempting to get unauthorized access via a false identity.
* **T**ampering: Aiming to modify data with an unauthorized method.
* **R**epudiation: Denying an action that a legal/illegal user conducts.
* **Information disclosure: Revealing confidential data.
* **D**enial of service: Making a specific service, system, or application unavailable.
* **E**levation of privilege: A user with restricted access rights receives more elevated privilege than they should.  
Figure 4: Main steps of the threat modeling approach.  
STRIDE can be used together with a the data-flow diagram (DFD) to describe the system entities, events, and the multiple levels of system zones [24]. As discussed in [3], there are five basic annotations are utilized to clarify the system elements using DFD:  
* **Process:** represents the system elements, input, output, and actions.
* **Data store:** represents a data storage or databases, which indicate the sources of the system's data.
* **Data flow:** represents the flow of the information between the different system entities that could represent a communication protocol.
* **External interactions:** indicates external elements that interact externally with the system elements.
* **Trust boundaries:** are used to divide the system structure into separate trust zones.  
STRIDE Per-Element  
This approach observes the threats according to applicable elements in the diagram. It concentrates on a certain set of threats for each specified system element. According to Table 1, Microsoft uses this table as a core of its security development lifecycle [26; 27].  
The \"X\" in Table 1 represents the class of threat that the element category could trigger. \"?\" means that the logging data store element is applied in addressing the repudiation, but in some cases, the logs could be used as a way for a repudiation attack. Consider the following scenario: there is a network data flow, and an attacker has access to the same network. In this situation, attackers can read, change, or broadcast a flood of packets in order to prevent a specific service from being performed [26].  
The second approach of the STRIDE method that identifies threats against the tuples: origin (i.e., source), destination (i.e., target), and interaction. This approach gets the same number of threats as STRIDE-per-element. However, the STRIDE per-interaction is more manageable because the threats could be more straightforward to comprehend than the STRIDE-per-element. This approach requires a reference chart of software to obtain the results [26; 27].
## 4 Optimization Method
### Improved Harmony Search  
Diverse versions of HS have been developed to improve its stability and the quality of the generated solutions. ImHS [50] is a variant that includes two modifications to enhance the performance of HS without affecting its simplicity. The first one is the change of the parameter bandwidth \(bw\), reducing it at each algorithm iteration to improve the exploitation process. Further, \(bw\) is personalized for each design variable considering the individual value ranges. The second modification is the inclusion of an additional parameter, the rate of improvement adjustment \(r_{iu}\), to give more variety to the process of combining different options to create new solutions, improving the exploration.
# Object-Wise Video Editing
## 2 Object Removal and Background Inpainting
### Prepossessing
#### 2.1.1 Object Mask Extraction  
Segmenting a foreground object from a video sequence is an important task in video analysis and editing in commercial applications. An unsupervised method of foreground segmentation is a fully automatic method that does not need any human intervention. The main source of this kind of segmentation is visual saliency [27] and motion like optical flow [28]. However, foreground object segmentation in a video sequence is often an ambiguous task because of a desired object is not fixed. Therefore, unsupervised object segmentation does not apply to the interactive video editing scenario.  
Figure 4: 3D point cloud and transformation matrix between frames are extracted from structure from motion (SfM) using all input video frames. Depth map of each frame is obtained by projecting 3D points and interpolating them at each 2D image space.  
Rather than concentrating on the unsupervised method, we extract the object mask using a method described in Reference [25] with given a reference frame object mask and input video. The goal of this method is to segment the target object that has to be removed from the entire video sequence. To make use of both the previous mask to generate the current frame and the reference frame that specifies the target object, a Siamese encoder-decoder network is described in Reference [25].
# Acoustic Emission Activity for Characterizing Fracture of Marble under Bending
## 5 Conclusions  
The present paper occupies mainly with the AE behavior of large populations of two marble types under bending and the repair efficiency as tested by a second loading after application of adhesive epoxy. The main conclusions of the work are summarized below:  
1. The fracture of the material follows three successive stages of high, low and high AE activity.
2. The AE parameters at each stage vary showing that the fracture pattern changes throughout the damage process even though the macroscopic failure of both marble types is derived from the same three-point bending setup.
3. Different marble types exhibit fairly different behavior, even from the initial stage of loading. Based on AE parameters from the early stage (AF and RA), an almost complete separation of the two populations (high and low strength) is possible, allowing the prediction of the strength group that each specimen belongs.
4. Repaired specimen with adhesive epoxy exhibited a restoration of flexural strength between 50% and 75% depending on the bonding efficiency.
5. The repaired specimens, which followed AE behavior similar to the intact ones (same three distinct fracture stages), exhibited much higher strength restoration (20% higher ultimate load at the test after repair), something attributed to the good adhesion that dictates the specimens to fracture similar to the intact ones.  
Study should continue with different fracture modes apart from bending, as well as with other natural stones like granite (different morphology, greater flexural strength and density than marble). In addition, pattern recognition approaches should be applied in order to achieve even better characterization or classification of the different AE populations.  
All authors contributed to the work presented in this paper, and to the writing of the final manuscript. [PERSON] contributed to the data analysis. [PERSON] was responsible for the ultrasonic experiment and the analysis of the data. [PERSON] provided discussion on the testing procedure and the results. [PERSON] organized the study and was responsible for the mechanical testing and AE monitoring.  
The authors declare no conflict of interest.
# Augmentation of Deep Learning Models for Multistep Traffic Speed Prediction
## 1 Introduction  
The intelligent transportation system (ITS) is an advanced tool designed to provide services related to traffic management and different modes of transportation, also enabling people to use the transportation network more intelligently and efficiently. The ITS is reflected as a key to the transport system. Traffic optimization control, network traffic planning, and accurate and real-time short-term traffic prediction in the road network play a vital role in analyzing the better traffic conditions of the road network. Several methods have been developed to enhance the efficiency and accuracy of the traffic prediction, e.g., parametric and nonparametric approaches [1].  
It is challenging to model the interaction between vehicles in large-scale transportation traffic in highly populated urban road networks, and collecting traffic data is also a difficult task. Existing short-term traffic prediction methods mostly use sensor location or corridor level, which cannot achieve network-wide prediction accuracy unless N models were trained for N nodes traffic networks [2]. For time series data, existing LSTM-based prediction models use only one hidden layer and, thus, relatively shallow structure [2; 3; 4]. Along with a corridor location, future traffic speed values are influenced by the past speed of downstream and upstream locations [5; 6; 7]. The predictive performance shall be enhanced by backward and forward temporal perspectives, particularly for recurring traffic patterns, by investigating the periodicity of time series data [6]. We cannot ignore the impact of downstream and upstream speed in each network location while predicting network-wide traffic speed instead of speed at one location. The learned features will be more widespread because of both backward as well as forward dependencies of spatiotemporal data. This research focuses on predicting network-wide traffic speed to better understand traffic states in a network instead of a single road link.  
We propose an AttBDLSTM-FCN model for the multistep traffic speed prediction over network-wide traffic data to overcome the aforementioned challenges. From experimental results, we come to know that our proposed model achieves better performance to predict network-wide traffic speed. In summing up, our contribution is as follows: we examined the traffic forecasting network-wide instead of at a specific location or many nearby locations. We proposed a deep architecture considering both forward and backward dependencies using AttBDLSTM-FCN to enrich the feature extraction and feature learning from time series spatial data. We examined the attention layer behavior in our model and concluded that using attention mechanism can enhance the model performance accordingly.  
The rest of the paper is as follows: Section 2 describes the literature review. Section 3 describes the methodology in great detail. Section 4 describes the results and discussion. Some interesting conclusions are drawn in Section 5.