esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Summary of the Project and Preprocessing of Data
### The Selection of Risk Assessment Indices
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Category Risk** & **Index** & **No.** & \(V_{1}\) & \(V_{2}\) & \(V_{3}\) & \(V_{4}\) & **Unit** \\ \hline \multirow{4}{*}{\(U_{11}\)} & \(U_{11}\) & Elastic deformation & \multirow{2}{*}{\(I_{1}\)} & \multirow{2}{*}{0–2} & \multirow{2}{*}{2–4} & \multirow{2}{*}{4–6} & \multirow{2}{*}{6–10} & \multirow{4}{*}{} \\  & energy index & & & & & & & & \\ \cline{1-1} \cline{5-10} \multirow{2}{*}{\(U_{1}\)} & \(U_{12}\) & Groundwater level & \(I_{2}\) & \(<\)60 & 60–90 & 90–120 & \(>\)120 & m \\  & \(U_{13}\) & Rock integrity coefficient & \(I_{3}\) & 0.75–1 & 0.5–0.75 & 0.5–0.25 & 0.25–0 & \\  & \(U_{14}\) & Gas monitoring data & \(I_{4}\) & 0–12.5 & 12.5–25 & 25–37.5 & 37.5–100 & mg/m\({}^{3}\) \\ \hline \multirow{4}{*}{\(U_{2}\)} & \(U_{21}\) & Cutterhead torque & \(I_{5}\) & 900–1500 & 1500–2100 & 2100–2700 & 2700–3300 & kN m \\  & \(U_{22}\) & Ventilation system flow & \(I_{6}\) & 1800–2000 & 1000–1800 & 500–1000 & 0–500 & m\({}^{3}\)/min \\  & \(U_{23}\) & Surrounding rock & \multirow{2}{*}{\(I_{7}\)} & \multirow{2}{*}{II} & \multirow{2}{*}{III(A/B)} & \multirow{2}{*}{IV} & \multirow{2}{*}{V} & \multirow{2}{*}{\multirow{2}{*}{R/min}} \\  & classification & & & & & & & \\ \cline{1-1}  & \(U_{24}\) & Cutterhead speed & \(I_{8}\) & 7–8 & 6–7 & 5–6 & \(<\)5 & R/min \\ \hline \multirow{4}{*}{\(U_{31}\)} & \(U_{31}\) & Entrance support effect & \multirow{2}{*}{\(I_{9}\)} & Very high & High & Moderate & Low & \multirow{4}{*}{} \\  & & stability & stability & stability & stability & stability & & & \\ \cline{1-1} \cline{5-10} \multirow{2}{*}{\(U_{32}\)} & \(U_{32}\) & Water seepage & \(I_{10}\) & Nil & Humidification & Trickle & & & \\ \cline{1-1}  & \(U_{33}\) & Protective layer thickness & \(I_{11}\) & 10–15 & 40–50 & 50–55 & 55–60 & cm \\ \cline{1-1}  & \(U_{34}\) & Guiding system & \multirow{2}{*}{\(I_{12}\)} & Zero & Tiny & Small & Large & \multirow{2}{*}{} \\ \cline{1-1}  & \(U_{34}\) & monitoring data & & deviation & deviation & deviation & deviation & \\ \hline \multirow{4}{*}{\(U_{41}\)} & \(U_{42}\) & Underground space & \multirow{2}{*}{\(I_{13}\)} & \multirow{2}{*}{0–0.1} & \multirow{2}{*}{0.3–1} & \multirow{2}{*}{0.3–1} & \multirow{2}{*}{1–2} & \multirow{2}{*}{mm} \\  & monitoring data & & & & & & & & \\ \cline{1-1}  & Underground pipeline & \multirow{2}{*}{\(I_{14}\)} & \multirow{2}{*}{No damage} & Tiny & Small & Large & \multirow{2}{*}{} \\ \cline{1-1}  & detection data & & & & & & & & \\ \cline{1-1}  & Surrounding building & \multirow{2}{*}{\(I_{15}\)} & \multirow{2}{*}{0–0.1} & \multirow{2}{*}{0.1–0.4} & \multirow{2}{*}{0.4–1} & \multirow{2}{*}{1–6} & \multirow{2}{*}{mm} \\ \cline{1-1}  & displacement & & & & & & & & \\ \cline{1-1}  & monitoring number & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Scoring criteria for various factor indicators.
## 2 Materials and Methods
### Evaluation of Remote Sensing (RS) Approach
#### 2.2.3 Statistical Analysis  
The statistical treatment of these results was performed using the 'MSTAT-C' software, version 6.0 (MSTAT-C, Michigan State University, MI, USA) with a significance level of 95% (\(p<0.05\)) and consisted of an analysis of regression between average pasture parameters and indices based in satellite imagery: (i) CP, NDF, and the PQI versus the NDVI\({}_{\text{RS}}\) and (ii) PMC versus the NDW\({}_{\text{RS}}\).
## 3 Development and Results of the Multi-Party Dialogue Management
### Development of Linguistically Nuanced Intent-Based Responses  
To provide linguistically nuanced responses in dyadic and group interactions, the knowledge expander was also used to generate variations of system responses (i.e., to achieve variation in conversation design) and responses for groups, since the hand-crafted foundation responses within the knowledge database were oriented toward individual users. We synthesized plural responses by instructing the GPT-3.5 to address a group of people based on singular responses (Figure 6):  
For example, the German sentence \"Du kannst mich [PERSON] nennen\" (\"You can call me [PERSON]\") was expanded into different paraphrases such as \"Mein Name is MIRA\" (\"My name is MIRA\") as well as different languages and into the plural for MPIs: \"Ihr konnt mich [PERSON] nennen.\" (\"You can call me [PERSON]\") (see example dialogue below). In total, the expanded knowledge database responses consisted of 1764 responses for the 222 intents with \(M=7.25\) different responses per intent, language, and context of dialogue (dyadic versus group). However, the challenge lies in crafting high-quality responses that accurately reflect the desired system behavior and transmitted information. The responses generated were carefully examined to ensure quality. However, we will address the challenges encountered in the following dialogue (Figure 7).  
Figure 6: The GPT 3.5 prompt to generate multiple English synonyms for plural sentences, addressing a group.  
As evidenced by the exemplary dialogue, certain translations and extensions were erroneous, resulting in difficulties in generating responses aligned with the intended intentions. For example, the response to the question about the gender of the robot was sensibly translated from German into English but was not transferred to MPI mode in German. Here, \"In my passport, it says digital information system\" in German MPI mode became \"In euren Passen steht, digitales Informationssystem.\" (\"In your passports it says digital information system.\"). We discovered the same issue with the German no-input phrase (see Section 2): \"Ich werde zuhoren, wenn ich grun leuchte\" (\"When my light is green, that means I'm listening.\") was incorrectly transferred to MPI mode as \"Wenn ihr grun leuchtet, hore ich zu\" (\"If you light up green, I'm listening\").
## 7 Examples
\begin{table}
\begin{tabular}{c c c c c} \hline \(n\) & \(e_{n}=\|u-\tilde{u}_{n}\|_{\infty}\) & \(\eta=\frac{e_{n}}{[u]}\) & \(100\%\times\eta\) & _Ratio_ \\ \hline
8 & \(5.20\times 10^{-2}\) & \(2.79\times 10^{-1}\) & \(27.95\%\) & \\
16 & \(7.32\times 10^{-3}\) & \(2.82\times 10^{-2}\) & \(2.82\%\) & \\
32 & \(2.02\times 10^{-3}\) & \(7.78\times 10^{-3}\) & \(0.78\%\) & \(3.6\) \\
64 & \(5.16\times 10^{-4}\) & \(1.98\times 10^{-3}\) & \(0.20\%\) & \(3.9\) \\
128 & \(1.30\times 10^{-4}\) & \(4.98\times 10^{-4}\) & \(0.05\%\) & \(4.0\) \\ \hline \end{tabular}
\end{table} 
Table 10: IE (50), \(\lambda_{A}=-0.5\), \(c=0.1\). QM: Trapezoidal rule. some results obtained in [26] are also quoted for comparison. The supremacy of the [PERSON]'s rule is reaffirmed and the agreement with results of other formulations is acknowledged._  
**Example 4**. : _Consider the integral equation [8]_  
\[u(x)-\int_{-1}^{1}(xt^{2}-x^{2}t)dt=-x^{4},\quad x\in[-1,1]. \tag{53}\]  
_The kernel \(K(x,t)=xt^{2}-x^{2}t\) is continuous on \([-1,1]\) and separable. Thus, Equation (53) can be solved exactly by the DCM in Section 3 to obtain_  
\[u(x)=-\frac{30}{133}x+\frac{20}{133}x^{2}-x^{4}. \tag{54}\]  
_We also solve this equation here by the collocation method (PM) explained in Section 6. Specifically, we use Lagrange interpolating polynomials of the type (38) to approximate \(u(x)\) and put the integral equation in (53) in the symbolic form (41). For \(n=2\), \(n=3\) and \(n=4\), we obtain_  
\[\tilde{u}_{2}(x) = -\frac{6}{19}x-\frac{15}{19}x^{2},\] \[\tilde{u}_{3}(x) = \frac{1}{9}-\frac{50}{171}x-\frac{470}{513}x^{2},\] \[\tilde{u}_{4}(x) = -\frac{30}{133}x+\frac{20}{133}x^{2}-x^{4},\]  
_respectively. Notice that for \(n=4\), as expected, the exact solution was fully recovered. This further validates the procedure in Section 6._  
**Example 5**. : _Let the integral equation [9]_  
\[u(x)-\lambda\int_{0}^{2\pi}e^{i(x-t)}u(t)dt=e^{x},\quad x\in[0,2\pi]. \tag{55}\]  
_The kernel \(K(x,t)=e^{i(x-t)}\) is continuous on \([0,2\pi]\) and separable, and therefore the integral equation in (55) can be written as_  
\[u(x)-\lambda e^{ix}\int_{0}^{2\pi}e^{-it}u(t)dt=e^{x}. \tag{56}\]  
_Following the steps in DCM in Section 3, we set_  
\[G=\lambda e^{ix},\quad\Psi(u)=\int_{0}^{2\pi}e^{-it}u(t)dt,\]  
_and write (56) in the form (14). By means of (5), \(\det\mathbf{V}=1-2\pi\lambda\), and hence the integral Equation (55) has a unique solution if \(\lambda\
eq\frac{1}{2\pi}\). In this case by substituting into (6), we obtain the unique solution_  
\[u(x)=e^{x}+\frac{\lambda(1+i)(e^{2\pi}-1)}{2(1-2\pi\lambda)}e^{ix},\quad \lambda\
eq\frac{1}{2\pi}.\]
## 4 Experimental Results
### Sensitivity Analysis on the Bundle Size  
Bundle_ratio is an adjustable parameter of the proposed scheme that compromises between randomness and locality of data references. As bundle_ratio increases (i.e., the size of a bundle grows), the randomness of data shuffling increases, thereby improving training efficiency, whereas the locality of data references is degraded from the buffer cache's perspective. When the bundle_ratio becomes 1, the proposed scheme works exactly the same as the default random shuffling method. Conversely, decreasing the bundle_ratio (i.e., reducing the size of a bundle) can improve the locality of data references from the buffer cache's perspective, but it degrades the effectiveness of training. If the bundle_ratio becomes extremely small, implying that each bundle consists of a single block, data references will be completely sequential without any randomness. In this case, cache performance can be maximized, but training efficiency degrades significantly over new epochs.  
Figure 8: Comparison of original and proposed schemes with respect to training efficiency.  
To investigate the effect of the bundle size, we conduct experiments and measure the performance as the bundle_ratio is varied. As shown in Figure 9, the best result was achieved when the bundle_ratio is at some specific point rather than at the extremes of being too large or too small. In this article, we set the default bundle_ratio to 0.1 based on our experimental results as it produces reasonably competitive results regardless of workloads. In reality, determining an optimal bundle_ratio for a given workload is a matter that we are still pursuing as it depends on various parameters including the dataset size and access characteristics. Thus, for now, we need to set a bundle_ratio that performs well for given workloads and will pursue the optimization issue as a future research.
# The Current State and Future Potential of Microbial Control of Scarab Pests
## 4 Bacteria
### Bacillus thuringiensis
[34] showed that a commercial formulation (wettable powder) of \(Bt\) gallariae sprayed on foliage reduced adult Japanese beetle feeding for up to 14 days after the initial spray. However, a granular formulation applied to the soil was less effective for controlling early and late-instar grubs. Importantly, foliar applications of \(Bt\) gallariae also showed nontarget effects on butterflies and caused 97-100% mortality of early instar monarch larvae. [PERSON] et al. [32] showed that a liquid spore/toxin formulation of \(Bt\) japonensis, that is not commercially available, effectively controlled oriental beetle and Japanese beetle grubs, as well as Asiatic garden beetle and to a lesser extent, European chater grubs in turf grass sites in Rhode Island. Given the large number of available toxins and the success of other \(Bt\) strains for managing non-scarab pests, the future development of scarab-specific \(Bt\) products is highly likely. The potential for these \(Bt\) strains to produce epizootics under natural conditions is not well-documented. While epizootics have been recorded for several lepidopteran populations [20,21], none have been observed in scarab populations. The scarab-specific effects found for some \(Bt\) toxins have largely been discovered through laboratory surveys, rather than observable epizootics.
# An LDPC-RS Concatenation and Decoding Scheme to Lower the Error Floor for FTN Signaling
## 1 Introduction
Section 3 details the structure of the proposed LDPC-RS concatenation code, RS parallel RS encoder and algorithm for parallel encoding coefficient calculation, and the perturbation BP decoder to lower the error floor. Section 4 describes the performed simulations and analysis. Section 5 concludes the paper.
# Traceability and Spectral Analysis of Transmission Error in Rotary Vector Planetary Gear Reducers
## 4 Transmission Error Spectrum Experiment  
### Test Setup  
The ZFUN ZRT-II reducer testing system, as depicted in Figure 4, was utilized to evaluate two distinct types of RV reducers. The angle sensor employed was a HEIDENHAIN ERA4282C series circular grating, and the data acquisition system utilized an NI-R series PCI-e7842 acquisition card. The reducer was mounted on the test bench in a configuration with a fixed pin housing, with input provided through the sun gear and output through the flange.  
The input shaft speed was set to \(n_{1}\) = 30 rpm, corresponding to a frequency of \(f_{\mathrm{sun}}\) = 0.5 Hz. Based on the previous analysis, the primary focus was on the characteristic frequencies listed in Table 3. The system sampling interval was set to 0.1 s, resulting in a sampling frequency of \(f_{\mathrm{s}}\) = 10 Hz. Data recording commenced after the output speed and transmission ratio stabilized following system startup, logging real-time data for time \(t\), input shaft speed \(n_{1}\), output shaft speed \(n_{2}\), input shaft angle \(\tau_{1}\), and output shaft angle \(\tau_{2}\). Dynamic testing of the TE was then conducted. Once the output shaft had rotated by 370\({}^{\circ}\), the input shaft reversed direction, with data recording continuing throughout this reverse operation.
## 3 Materials and Methods  
### Input Data  
The presented analyses are based on the complete 250 m resolution Terra MODIS Enhanced Vegetation Index (EVI) time series covering the years 2000 until 2018. Vegetation indices (VI) are established tools to highlight vegetation vigor and productivity while reducing the effects of acquisition geometry, atmospheric conditions, and soil properties on the remotely sensed signal. Since our own pre-analyses indicated a higher robustness of the EVI in comparison to other VIs such as NDVI, this metric was chosen for further analysis. The EVI has a value range from \(-1\) to \(1\), with values below \(0\) generally corresponding to non-vegetated areas. The EVI is, like other VIs, a proxy for the vitality and productivity of the vegetation. However, it is also influenced by the structure, density and phenological stage of the vegetation, which is why we do not use absolute but relative EVI values to investigate the vegetation condition in this study. Sixteen-day composites of EVI and the corresponding pixel reliability layers were extracted from the MODIS product MOD13Q1, version 6 [33]. All available 16-day composites per year were included in the multi-annual MODIS time series, starting with the composite of 18 February 2000 and ending with the composite of 19 December 2018. The composite dates represent the starting dates of the 16-day MODIS EVI composites.  
In order to assess the effect of agricultural drought conditions on crop production in Germany, crop yield statistics from the federal statistics agency of Germany (Destatis) were used. The annual yields per hectare for Germany for four different crop types (winter wheat, rye and winter cereal, silage maize, and potatoes) were examined [31]. The CORINE land cover (CLC) map of 2012 Version 18.5.1 and the CORINE land cover change (LCC) layers (2000-2006 and 2006-2012) Version 18.5 [32], all with a spatial resolution of 100 m, were used for masking and for land cover specific analyses.
# Sustainable Production of Building Blocks by Reusing Stone Waste Sludge
## 1 Introduction
When employed for the substitution of fine and coarse aggregates, marble waste can reach higher percentage contents, between 50% and 75%, optimizing the reuse of higher quantities of waste. Nevertheless, the geological nature of the coarse aggregates and the particle size distribution of the fine aggregates replaced by dust influence the final properties. It should be noted that the different chemical and mineralogical characteristics of the raw material from which SWS is produced determine the most appropriate application scenarios. In particular, it is possible to distinguish SWS from marble, SWS from granite, and SWS from limestone. Among the most common reuse scenarios are the following: the packaging of bricks and tiles with marble SWS [38; 39] and granite SWS, sometimes fired with clay mixtures; the packaging of ceramics and stoneware with granite SWS [40]; and the packaging of concretes and mortars with marble SWS [41] and limestone SWS. However, little research has been performed on the possibility of using limestone SWS in the formulation of mixtures for the production of building blocks. Therefore, the contribution aims to investigate the possibility of manufacturing building blocks with the use of limestone SWS and products through simple and low-impact processes. With reference to the issues outlined, the paper describes the research pursuing the following objectives:  
1. Reduce the amount of raw materials mined, such as sand, for the production of mortars and cements. 2. Reduce the amount of waste disposed of in landfills. 3. Experiment with high-performance, low-cost construction materials with simple production processes. These objectives have been achieved through the study and experimentation of innovative mixtures for the production of concretes and blocks using SWS aggregates that give high performance and recover waste from the supply chain. The innovation of this study lies in the mix design approach, the production process, and the evaluation of the resulting properties. These characteristics have been compared with the current Italian regulations, and the potential applications of the tested products in the construction sector have been identified. The aim is to guide the construction and building materials industry towards innovative and circular solutions for the production of more sustainable building elements through the use and valorization of SWS. The results obtained show that it is possible to use significant quantities of such waste to produce building blocks that can be manufactured using few raw materials, without additives and using simple and inexpensive production processes. For example, no baking or energy-intensive production processes are required.
# On the Hardware-Software Integration in Cryptographic Accelerators for Industrial IoT
## 2 Preliminaries and Related Work
### Openssl Library  
OpenSSL [17] is a widely adopted, user-space library that provides software implementations for most of the cryptographic algorithms and protocols such as AES, RSA or TLS. The library exposes one (or more) API(s) calls for each operation; developers who want to add cryptographic operation in their application just need to initialize the structures and invoke them in the correct order.
# Understanding the Light Detection in an Integrated Novel Electrophotonic Wavesensor Photodetector
## 4 Electrical Behavior and Photodetection
### Energy Band Behavior on Dark Condition  
Figure 4 illustrates the energy levels of the conduction band (\(E_{C}\)), valence band (\(E_{V}\)) and the Fermi level (\(E_{F}\)) along source, bulk and drain for a WS with p-type substrate. The evaluation is at a middle depth in the n+ wells, where surface effects are negligible.  
At this initial condition, drain current is not established due to the potential barriers. When the WS is biased with \(V_{DS}\) > 0 V, band bending is produced and probability of carrier injection is increased. The drain voltage produces a drain-substrate junction reverse biased, increasing the width of depletion. As the drain voltage increments, the depleted zone in the drain increases, until both drain and source depletion regions overlap. This is known as Punch-Through condition, at \(V_{DS}\) = \(V_{PT}\). The value of \(V_{PT}\) is proportional to the separation L between source and drain. At Punch-Through, the voltage barrier reduces, as shown in Figure 5. Then, an electric current can be established due to the higher carrier injection probability.
# Detection and Localization of Overlapped Fruits Application in an Apple Harvesting Robot
## 5 Experiments
### Evaluation Criterion  
#### 5.2.1 Area Relative Error  
The size of the apple's area can guide the robot's harvesting sequence. It is proportional to the distance to the camera. The larger the area and the closer the distance, priority is given to harvesting, otherwise, the farther the farther the apple is, the harder it is to harvest; therefore, at this time, the robot needs to adjust its position. Therefore, the relative error of the area is small, indicating that the positioning and recognition accuracy is high.  
The area of target apple is defined as the total number of pixels in the contour of apple. The area difference between the actual apple and the fitting-out circle is defined as the absolute error \(\Delta E_{s}\), and the ratio of the absolute error and the area of actual apple is defined as the relative error \(r_{s}\).  
\[\Delta E_{s}=|S_{1}-S_{2}| \tag{6}\]  
\[r_{s}=\frac{\Delta E_{s}}{S_{1}} \tag{7}\]  
where, \(S_{1}\) is the number of pixels in actual apple, \(S_{2}\) is the number of pixels in the apple contour.
## 3 Data and Methods
### Comparison of DEM Quality with Image Texture  
Typically, DEM quality is assessed with external geodetic measurements by observing hillshade images and by other techniques looking at inter-pixel variability (e.g., [2; 15]). Numerous studies detail statistical analyses of DEM quality (e.g., [3; 43]), and here we present an approach unique to our study area that has the goal of demonstrating a chief cause of DEM quality difference between different algorithms and parameters: image texture.  
Image texture, or the local difference in digital number values (converted to PAN radiance in Figure 2A using SPOT6 metadata), is known to cause issues with image correlation and has been defined mathematically in a number of ways (e.g., [26; 41]). Some authors define image texture with respect to the orientation and magnitude of changes in pixel value (e.g., [44; 45]). Here, we define image texture at a pixel independent of orientation as the variance of the PAN 12-bit digital numbers in a local window (3 \(\times\) 3, 5 \(\times\) 5, 7 \(\times\) 7, or 9 \(\times\) 9 pixels). Prior to calculation of the PAN variance, we resample the orthorectified (map-projected) PAN images to match the DEM resolution (e.g., 3 m), so that all pixel variances match between the PAN image and output DEM. For the texture calculation, we use only the SPOT6 B-scene as this is near the nadir, but results using the A or C scene are comparable.  
In addition to the PAN variance variable, we also find the DEM variance in the same pixel window on the salar. The salar is locally a perfectly flat surface and should have near zero variability in height (cf. Figure 1C). To retrieve the matching DEM variance at a pixel, we employ a least squares plane fitting procedure in the same window (\(3\times 3\), \(5\times 5\), \(7\times 7\), or \(9\times 9\) pixels) as the PAN variance and then calculate the point-to-plane distance of all elevation pixels in the window and take the variance of these distances. In effect, this corrects for any local tilting of the salar, and only looks at the erroneous height variations, which we assume should be near zero.  
The combination of these variables on the salar allows us to explore the relationship between DEM quality (measured here as DEM variance on a flat surface, akin to the \"runway\" method of DEM quality estimation using flat airport runways [46]) and image texture (measured here as PAN variance) on a smooth, flat, vegetation-free surface. We note the salar has highly variable image texture due to 10-1000 m length-scale variations in salt patches and wind-blown dust.