esa-sceva/satcom-chunk-collection huggingface dataset:
# Indoor Localization for Augmented Reality Devices Using BIM, Point Clouds, and Template Matching
## 3 Methodology
### BIM Distance Map  
To prepare a BIM model for localization, the 3D model has to be transformed into a 2D map for template matching. For every floor of the BIM model, we set a cross section over the \(XY\) plane, which is the basis of the template-matching map. The cross section is placed at roughly the same height as a device would be held above floor level. Since point-cloud data gathered from depth sensors is inherently noisy and the environment may be obstructed by furniture, the recorded point cloud will differ from the original BIM model. These spatial inaccuracies are not accounted for template matching against a binary map representation of the BIM model. An error of 10 cm would have the same negative impact on the correlation as an error of 1 m. Therefore, we match against the inverse distance map of the BIM model. The inverse distance map allows for correction of small errors in the point cloud when template matching while still penalizing inaccuracy. The distance map for the model can also be calculated and cached beforehand and has no impact on efficiency.  
To calculate the inverse distance map, every cell position of the distance map is assigned a 3D coordinate on the cross section of the floor. For every cell position \(u_{i,j}\), we find the closest point on a wall \(v_{i,j}\). The distance to the closest wall equals \(d_{i,j}=\|u_{i,j}-v_{i,j}\|\) for every cell. We then calculate the inverse distance map over all pixels \(i,j\):  
\[\hat{d}_{i,j}=\frac{1}{d_{i,j}+1} \tag{1}\]  
Figure 1: Algorithm overview: \(\vec{p}\) is the processed point cloud; \(\vec{n}\) is the calculated normals; \(\phi_{k}\) is the rotation candidates; \(g_{i,j}\) is the grid map over the pixel indices \(i,j\); \(d_{i,j}\) is the inverse distance map of the building model; and \(T\) is the final transformation matrix. The algorithm can run continuously on the device and update when new point-cloud data is available.  
which results in higher values the closer the position \(i,j\) is to a wall. Figure 2 shows an example of the inverse distance map. The closer a pixel on the template is to a wall, the higher the correlation value. Large environments may require a cutoff distance to avoid penalizing inconsistently as the center of a room would be penalizing outliers much more harshly than other places.
## 4 Materials and Methods
### Analysis of Norisoprenoids in Berries Using SPME-GC-MS  
About 50 g berries with the seed removed were blended with 1 g PVPP and ground into powder in liquid nitrogen. The extraction of free and total norisoprenoid was conducted following published research with some modification [50]. For free norisoprenoid, we weighted 1 g berry powder and put it into a 20 mL autosampler vial with 5 mL of citrate buffer (0.2 M, pH 3.2, saturated with NaCl) and 10 uL internal standard (1.008 mg/L 4-Methyl-2-pentanol) added. Then the vials were tightly capped and equilibrated at 50 \({}^{\circ}\)C in a thermostatic bath for 15 min. To extract total norisoprenoid, 1 g berry powder was put into a 20 mL autosampler vial with 5 mL of citrate buffer (0.2 M, pH 2.5, saturated with NaCl) and 10 uL internal standard (1.008 mg/L 4-Methyl-2-pentanol) added. Then the vials were tightly capped and equilibrated at 99 \({}^{\circ}\)C in a thermostatic bath for 1 h.  
The volatile compounds were extracted by headspace-solid phase microextraction (HS-SPME) using 2 cm DVB/CAR/PDMS 50/30 um SPME fiber (Supelco, Bellefonte, PA, USA) at 40 \({}^{\circ}\)C for 30 min with stirring. An Agilent 6890 gas chromatography coupled with an Agilent 5975C mass spectrometer was used to analyze the volatile compounds in the samples according to the method described by [PERSON] et al. [51]. The compound separation was achieved with an HP-INNOWAX capillary column (60 m \(\times\) 0.25 mm \(\times\) 0.25 um, J & W Scientific, Folsom, CA, USA). Volatile compounds were identified by comparing mass spectrums and retention time with the available external standards. The compounds with reference standards were identified by comparing their retention indices and mass spectrums with the NIST11 database. Quantitation followed our published method [52]. The volatile compounds with available standards were quantified based on their reference standards, whereas the volatiles without available standards were quantified using standards that had the same functional groups and/or similar numbers of carbon atoms.
## 3 Materials and Methods
### Evaluation Metrics  
Each data instance--in this case, an image--has four potential outcomes that are categorized, which are true positive (TrPos), true negative (TrNeg), false positive (FaPos), and false negative (FaNeg). A TrPos indicates that the model accurately predicted an optimistic class and a TrNeg suggests that the model accurately predicted a negative class. A FaPos denotes that the model produced an overly optimistic class forecast. Like the FaPos, a FaNeg implies that the model, in error, made a negative class prediction [70].  
TrPos and TrNeg are the perfect examples in the medical industry since they involve a patient classification for the ill person and a patient classification for the healthy person. The FaNeg example, however, is the most severe instance where a sick person may be mistakenly labeled as healthy rather than unwell. This misclassification in some disorders can be fatal. In the DR, misdiagnosis can result in significant disease progressions that could eventually result in blindness.  
The most widely used statistic for assessing a model's effectiveness at making predictions is its accuracy score (Equation (1)). A loss metric measures the model performance, the sum of mistakes on each occurrence in a training or testing dataset. The precision  
Figure 2: Overview of the transfer learning models.  
metric concerns how many positive cases are labeled as positive or how many patients with DR are indeed DR predictions. This score reflects how accurately the model identified the disease from the data. Precision (Equation (2)) is typically utilized when it is necessary to ensure that good forecasts come true.  
\[\text{Accuracy}=(\text{TrPos}+\text{TrNeg})/(\text{TrPos}+\text{TrNeg}+\text{ FaPos}+\text{FaNeg}), \tag{1}\]  
\[\text{Precision}=\text{TrPos}/(\text{TrPos}+\text{FaPos}), \tag{2}\]  
On the other hand, the specificity (Equation (3)) meter, or TrNeg rate, displays the number of negative classes accurately identified as such, or in our case, the number of healthy people correctly identified as such. A false alarm with a moderate cost would be to label a healthy person as ill. However, if the condition is serious, telling the person about it could set off a panic attack that could be harmful depending on his physical and mental health. Therefore, in some circumstances, having certainty about the diagnosis is advantageous before making it public, which is what the specificity metric offers.  
\[\text{Specificity}=(\text{TrNeg})/(\text{TrNeg}+\text{FaPos}), \tag{3}\]  
Among all the real positive cases, recall (also known as sensitivity or TrPos rate) (Equation (4)) determines the positive points that were correctly anticipated. The recall measure is significant and necessary in medical applications where the cost of the incorrect prediction, particularly the false negative instance, is quite expensive, especially when the disease has considerable side effects or is contagious and rapidly spreading.  
\[\text{Recall or Sensitivity}=\text{TrPos}/(\text{TrPos}+\text{FaNeg}), \tag{4}\]
## 2 Materials and Methods
### Dataset, Model Characterisation, and Inputs  
The case-study building for this paper is an office building on the riverside of a recently modernized district in Lisbon, Portugal. Built in 2007, it has a net floor area just under 12,000 m\({}^{2}\), with 7000 m\({}^{2}\) over eight floors (above ground) occupied by offices and open workspace area, and 5000 m\({}^{2}\) over three (underground) floors dedicated to parking. All building facades are mostly glazed and exposed to the elements, with partial shading installed on the south-facing facade only. The roof consists of a polystyrene insulated concrete slab covered by an outer layer of pebbles. The building is equipped with an advanced BMS, including a building automation and control system (BACS). A nearby trigeneration plant provides hot and cold fluids used to heat and/or cool the building, which are transferred through heat exchangers installed on a thermal substation located on the underground floors.  
A significant amount of data was collected, including weather-related data (outdoor dry-bulp temperature, relative humidity, wind speed, direct normal solar radiation, diffuse horizontal radiation, occupation (hourly profile), and enthalpic counts of (hot and cold) fluid consumed from a complete year, obtained through simulation of a calibrated building energy performance model. In this area, the cooling period is usually April to September, while the rest of the year is the heating period. Around 4/5 of the data thus obtained was used in model training, while the remaining 1/5 was used for testing (heating and cooling) demand forecast. Both historical data (namely 7-days and 14-days prior) and exogenous variables were considered in the models. Variable normalization was performed, using training dataset maxima and minima for each variable as scaling factors.  
Different combinations of inputs were tested in this study and their respective error metrics (mean absolute error, MAE, and root-mean-square error, RMSE) were compared to evaluate the most effective set of inputs to forecast heating and cooling energy demand. In Tables 1 and 2, the combinations of features tested for the cooling (Cx) and heating (Hx) energy demand, respectively, are presented.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Feature** & **C1** & **C2** & **C3** & **C4** & **C5** & **C6** \\ \hline Historical cooling load from 1 week before [kW] & X & & X & X & X \\ Historical cooling load from 2 weeks before [kW] & X & & X & X & X \\ Outdoor air temperature [\({}^{\circ}\)C] & & X & X & X & X \\ Relative humidity [\%] & & X & & & X \\ Estimated occupancy profile & & X & & X & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sets of features tested for cooling energy demand forecast.
## 3 Adaptive Transmit Diversity Strategy
### Adaptive Selection Merge Algorithm  
High inter-beam interference is a key feature of multi-beam array systems. Figure 2 shows the coverage area of neighboring LEDs and the inter-beam interference therein. At the receiving side, the interference of the user \(k\) by non-target beams can be measured as:  
\[\rho_{k,m}=\frac{h_{k,m}}{\max(\mathbf{h}_{k})},m\in[1,M], \tag{11}\]  
where \(\mathbf{h}_{k}\) is the \(1\times M\) order channel gain matrix of user \(k\) with different LEDs, and \(\frac{h_{k,m}}{\max(\mathbf{h}_{k})}\) denotes the ratio of the non-target beam gain to the target beam gain. In order to determine the degree of influence of inter-beam interference, a threshold \(\varepsilon,(0<\varepsilon<1)\) is set, when \(\rho_{k,m}>\varepsilon\), it indicates that the inter-beam interference is more serious. The LEDs on the  
Figure 5: Block diagram of the adaptive transmit diversity strategy system.  
transmitting end need to be selected for merging employing Algorithm 1. The algorithm is expressed as follows:  
```
Input: Channel gain matrix \(\mathbf{H}\). Output: LED combine index set \(\mathbf{L}\). Initialization: Inter-beam interference tolerance threshold \(\varepsilon\) = 0.27, beam interference ratio \(\rho_{k,m}=0\), LED combine index set \(L_{0}=\varnothing\), index \(C_{0}=\varnothing\), \(m=1\), \(k=1\), \(i=1\), \(j=1\). Steps 1: Find the element \(h_{k,m}\) with \(\rho_{k,m}=1\) in each user in the channel gain matrix \(\mathbf{H}\). The strongest light signal comes from the corresponding LED, which is labeled as \(C_{m},m\in[1,M]\). Steps 2: Find the LED corresponding to the element \(h_{k,m}\) in the channel gain matrix \(\mathbf{H}\) where each user satisfies \(\rho_{k,m}\geq\varepsilon\). If the LED belongs to the index set \(C\), it is labeled as \(l_{m},m\in[1,M]_{m},m\in[1,M]\), \(L_{k}=\{l_{m}\}\). Steps 3: Updating the index set \(\mathbf{L}=\{L_{k}\}\), \(k=k+1\). Steps 4: If \(k>K\), the iteration stops. Otherwise go to Step 2 to continue. Steps 5: Compare each subset of the index set \(\mathbf{L}\) and merge the intersecting subsets until each subset of the index set \(\mathbf{L}\) no longer intersects.
```  
**Algorithm 1**Adaptive Selection Merge Algorithm  
By selecting the merge in Algorithm 1, the index set \(\mathbf{L}\) is obtained, and each subset in \(\mathbf{L}\) is the set of LEDs with higher inter-beam interference, and the LED combinations are merged, while the served users are merged into a cluster accordingly. The transmit diversity is used within the LED groups to send the same data, and the space-split beams with low inter-beam interference are used between different LED groups, which balances the frequency efficiency and BER performance of the optical multi-beam NOMA system.
## 2 Materials and Methods
### System Model for Disturbance Estimator Design  
It can be easily found that equation is obtained by the common assumptions. The rotor's magnetic field is sinusoidal in the air gap space. The saturation of stator core, core Eddy current, hysteresis loss, and damping of rotor windings are ignored. It is also assumed that the magnetic circuit is linear. However, these factors will affect the performance of PMSM in the actual operating environment. Simultaneously, the nonlinear terms in system (2) also deteriorate the operating effects. Linearizing the nonlinear system with linear equation will make the research relatively simple, but it cannot completely reflect the complicated actual situation, so the accuracy of the calculation result is relatively low [18].  
In this subsection, we will discuss the effects by modeling and quantifying these factors.  
Define that \(x^{T}=\left[\begin{array}{cc}i_{q}&\omega_{m}\end{array}\right]\), \(y=x^{T}\), \(u=u_{q}\), then one can get that  
\[\begin{array}{l}\dot{x}=Ax+Bu+E\xi\\ y=Cx\end{array} \tag{4}\]in which, the parameters can be obtained by Equation (6) as \(A=\left[\begin{array}{cc}A_{1}&A_{2}\end{array}\right]^{T}\!A_{1}=\frac{1}{L_{ \delta}}\!\left[\begin{array}{cc}-R&-p_{n}\psi_{f}\end{array}\right]\), \(A_{2}=\frac{1}{J}\!\left[\begin{array}{cc}\frac{3p_{n}\psi_{f}}{2}&0\end{array} \right]\), \(B=\left[\begin{array}{cc}\frac{1}{L_{\delta}}&0\end{array}\right]^{T}\!,E= \left[\begin{array}{cc}0&-\frac{1}{J}\end{array}\right]^{T}\!,C=\left[\begin{array} []{cc}1&0\\ 0&1\end{array}\right]\), \(\xi=T_{L}\).  
Then, it can be assumed that the influence of fore-mentioned factors, including saturation of stator core, hysteresis loss, and so on, can be quantified in the system load disturbances. The details of considering the factors are as follows.  
\[\dot{x}=Ax+Bu+E(\xi+\delta) \tag{5}\]  
where \(\delta\) represents the load disturbances. Define that \(d=\xi+\delta\). One can get that  
\[\dot{x}=Ax+Bu+Ed \tag{6}\]  
Based on the model (6), the observer and estimator can be constructed to get the load, disturbance value, and the estimation of \(x\). It will be further discussed in the next section.  
In practice, the dynamic change of load will cause load disturbance, and the output speed of motor will also shake, which greatly affects the motion accuracy and working stability of PMSM drive system [19]. How to suppress this load disturbance caused by load change is what this paper is doing.
## 4 Experimental Validation and Project Examples
After collecting data on-site using digital panoramic borehole camera technology, the collected results were analyzed. There are three vertical boreholes on a landslide surface, hole numbers ZK1, ZK2 and ZK3, and their orifice coordinates are (12259.532, 88099.958, 1707.729), (12299.435, 88111.734, 1708.499). The survey depth of each borehole is 70m. Take the mean value of the three borehole coordinates as the coordinate origin, the  
Figure 12: Coordinates diagram of the structural plane in sample 1. Figure 13: Coordinates diagram of the structural surface in sample 2.  
due north direction as the \(x\)-axis, and the vertically downward direction as the \(z\)-axis to establish a suitable coordinate system. A suitable coordinate system was established to facilitate subsequent data processing and reduce errors. Furthermore, after a coordinate transformation, the orifice coordinates were converted to ZK1 (\(-\)34.452, \(-\)7.623, 0.810), ZK2 (5.451, 4.153, 1.580), and ZK3 (29.002, 3.471, \(-\)2.391), the relative position diagram of the boreholes are shown in Figure 14. After processing the analyzed structural plane data, the data were plotted into the 3D coordinate system to obtain Figure 15. From the figure, it can be found that the structural plane coordinate points are not uniformly distributed, and the dense areas of their distribution are consistent with the statistically obtained dominant production grouping of the structural plane. According to the calculation formula, determine the appropriate \(\varepsilon\) value, which is 5 here. Combined with the occurrence and depth information, we can quickly screen out some structural planes that meet the consistency from a large number of data. Table 5 below shows the seven possible coherent structural planes screened. By using the structural face information and finding the corresponding borehole images, the coherence of the structural faces can be further judged. By comparing the geological characteristics of the structural faces, the following four groups of more consistent structural faces were found, as shown in Figure 16 below, among which (a) (b) and (d) are all open fissures with the same lithology on both sides of the structural faces, the geological characteristics are consistent, and the openness of the structural faces are similar, and (c)  
Figure 14: Relative position diagram of the three boreholes. Figure 15: Coordinates diagram of the structural surface in the three boreholes.
## 3 Experiments
### Compression and Reconstruction
In Table 2, we denote CBN-VAE, CBN-VAE-b and CBN-VAE-c. Table 3 shows the results of the performance of our model on different datasets. The results show that the proposed model can achieve higher CR value and higher SNR value than other compression algorithms. At the same time, the proposed model can achieve efficient compression performance for different categories of data in different datasets.
# Comparative Study of Blockchain Hashing Algorithms with a Proposal for HashLEA
## 3 Background
### Hash Functions
#### 3.3.10. Grostl  
Grostl is a cryptographic hash function that operates as an iterated hash function, utilizing a compression function for processing message blocks. The design principles of Grostl are fundamentally distinct from the SHA family, incorporating innovative elements while drawing upon components from the AES block cipher. In particular, Grostl utilizes the identical S-box as that employed by AES and constructs its diffusion layers in a comparable manner. However, it separates from this approach by using a small number of permutations rather than the numerous permutations used in block ciphers. This results in a number of benefits, including simplified analysis, robust security, resistance to side-channel attacks, efficient parallelism, and protection against length-extension attacks [73].  
The Grostl algorithm employs the wide-pipe construction, which represents an enhancement to the fundamental Merkle-Damgard design through the utilization of larger internal state registers. This construction enables the execution of two parallel functions, designated as \(P\) and \(Q\). Each function executes a series of four round transformations on message blocks [74]. The initial transformation, designated as \"AddRoundConstant\", introduces a constant value specific to the current round through a bitwise XOR operation. SubBytes is the subsequent non-linear substitution step that employs the identical S-box as the AES algorithm. The subsequent transformation, ShiftBytes, performs a cyclic left shift on rows of the internal state matrix. Finally, MixBytes applies matrix multiplication to achieve diffusion, thereby ensuring that each output bit depends on multiple input bits. These transformations are executed in a sequential manner over a number of rounds, with 10 rounds being the minimum for 224- and 256-bit outputs and 13 rounds being the minimum for 384- and 512-bit outputs. The processes of \(P\) and \(Q\) operate on message blocks that are represented as either \(8\times 8\) or \(8\times 16\) byte matrices, depending on the length of the hash [75].  
The Grostl compression function, designated as \(f\), operates on two inputs: a chaining input and a message block \((f(h,m)=P(h\oplus m)\oplus Q(m)\oplus h)\), where \(h\) is either the initial value (for the first message block) or the output of the previous computation, and \(m\) is the current message block. The compression process is carried out by iterating over the padded and divided message \(M\), using the chaining value from the previous iteration to process the next block. Once the final message block has been processed, the output transformation computes the final hash value by applying the following equation: \(\Omega(x)=P(x)\oplus x\), truncating the result to the desired hash size [76].
## 6 Conclusions and Future Work  
In this paper, the use of FEC codes in a railway application of carbody SHM was investigated, which can be extended to other infrastructure of the railway industry as well. A wireless acoustic emission system was described, namely the Micro-SHM system, which obtains specific features and transmits them via telecommunications, in order to  
Figure 8: Throughput of different SNR values for RS and Polar codes.  
Figure 7: FER of different SNR values for RS and Polar codes.  
be forwarded in a central processing unit. The assumption is that the transformation of the signal to features occurs in the device. The case is to utilise 5G communication for our communication; note that the Micro-SHM can come with 4G capability.  
Due to noisy channels, FEC codes were used to reduce errors in communication. The new Polar codes were encapsulated and were compared with the RS codes, which are good in terms of performance. We encapsulated the AFF3 CT simulator to run simulations and we obtained the BER, FER, and throughput for a range of SNR. The POLAR codes are better for the particular application in terms of these metrics.  
For future work, the aim is to put the \(\pi/2\)BPSK modulation to the simulator and run more experiments and comparisons with FEC codes including Turbo and LDPC codes. Moreover, we will also use machine learning to optimise the decoding process of the FEC codes.  
Finally, The successive cancellation (SC) decoder used in POLAR codes shows advantages in complexity, but is limited by its bit-by-bit decoding strategy. We aim to investigate a more thorough decoder to overcome the limitations of the current approach. The network construction cost is going to be overcome by using a custom 5G network, which will be utilized for the purpose of the application only.  
Methodology, validation, investigation, writing--original draft preparation, data creation: E.D.S. Acoustic Emission Structural Health Monitoring methodology, review, editing, supervision: V.K. All authors have read and agreed to the published version of the manuscript.  
This research was co-financed by the European Regional Development and Greek national funds Fund under the framework of the Operational Program <<Central Macedonia 2014 2020>> (Project code: KMP6-0077768.  
The data that has been used in this paper were produced from the AFF3 CT simulator. The link of this simulator can be found in the references and the manner with which to replicate the data is found in the manuscript.  
The authors declare no conflict of interest.