esa-sceva/satcom-chunk-collection huggingface dataset:
# Camera-Sourced Heart Rate Synchronicity: A Measure of Immersion in Audiovisual Experiences
## 3 Methodology
### Permutation Testing Implementation  
To test the significance of any observed difference in the average ISC-HR value between groups, a permutation test was implemented [70]. The test works by initially calculating the observed difference in the median and then randomising the group labels and comparing the difference in the median for this new permutated version of the dataset. This process of randomisation and comparison is repeated 100,000 times to generate a consistently reproducible likelihood value, which serves as an alternative to the p-value obtained from a Mann-Whitney U test. The median is used rather than assessing the difference in terms of mean, as it is a way of generating an average that is more robust to outliers.  
To calculate the significance of any observed relationship between ISC-HR values and any continuous feature measured using the survey, a similar permutation correlation test was implemented [70]. The test works by initially calculating the observed Spearman's rank correlation coefficient (SCC) [71] between ISC-HR and the chosen feature and then randomising the order of the ISC-HR values before comparing the difference SCC for this new permutated version of the dataset. This process of randomisation and comparison is repeated 100,000 times to generate a consistently reproducible likelihood value, which serves as an alternative to the p-value obtained from a single Spearman correlation test. SCC is used instead of PCC as it is a more robust way of assessing correlation because it is less sensitive to outliers.
# Urban Free-Space Optical Network Optimization
## 4 Results
### Simulation Framework  
To simulate large-scale visibility graphs efficiently and realistically, the Networkx library tool was used in Python. This library contains a wide range of graph types, among them simple graphs, random graphs, geometric graphs, and weighted graphs. The Networkx package also includes common algorithmic implementations [27]. One example is the implementation of the 2-factor approximation method to the Steiner problem. This research deals with facility-location problems in urban regions. To test the heuristic methods presented in this study, we used the Open Street Map (OSM) platform, an open source geo-database that holds urban information [36]. From this database, we extracted information essential for building visibility graphs on a city terrain, such as building locations and heights.  
Figure 12 presents a 3D representation of OSM, from which we were able to construct the visibility graph.  
Figure 12: A 3D map of Manhattan Island, as defined in Open Street Map (OSM). Each building is stored as a 3D shape, allowing us to compute the associated visibility graph.  
The visibility graphs were constructed using two main methods: (1) generating random visibility graphs and (2) constructing visibility graphs based on urban data (e.g., open street maps).  
All the experiments were run on an Apple MacBook Air with Intel Core i7 processor (quad-core).
## 3 Results and Discussion  
### Reduction in pH and Accumulation of Volatile Fatty Acids during Liquid Digestate Neutralization  
The LDN process significantly (\(p<0.05\)) reduced the pH from 9.3 to 7.5 during 60 h (Figure 1a), indicating that the method could effectively decrease the pH of the alkali-treated CS. There were two drivers behind this reduction. First, the direct and immediate neutralization reaction caused the pH of the lime-treated CS to drop from approximately 10.5 to 9.3 during the mixing process at 0 h (omitted from Figure 1). Second, the LD contained sufficient acid-producing microbes to further reduce the pH by continued acid production during subsequent incubation. Notably, LD arguably contains not only acid producers but also acid consumers. However, the LDN process was carried out under non-strictly anaerobic and overloaded conditions, which were not conducive to the activity of acid-consuming microorganisms, thus, breaking the imbalance between the acid production and consumption rate [30; 31]. Consequently, we utilized this imbalance to decrease the pH and stopped it in time. This was also conductive to avoid over-acidification, which could affect the subsequent AD.  
As shown in Figure 1a, a certain amount of acetic acid appeared at 0 h, which was one of the by-products generated during the alkali treatment process [32]. Acetic acid first decreased gradually from 9.8 to 5.5 g/kg\({}_{\text{fresh\, matter}}\) within 24 h, then gradually accumulated and finally reached 14.5 g/kg\({}_{\text{fresh\, matter}}\) at 60 h. As for isobutyric acid, it started appearing after 36 h, and finally reached up to 3.3 g/kg\({}_{\text{fresh\, matter}}\) at 60 h. Surprisingly, no accumulation of propionic, n-butyric, and valeric acid was identified throughout the process. This finding contradicts the previous reports on over-acidification [30; 33], seemingly because the pH of the LDN process did not drop to 6.5, or even lower, as typically observed in the case of over-acidification. The production of these VFAs was responsible for the reduction in pH, but it is worth noting that the drop followed by the rise in acetic acid implied that part of the biomass that could have produced biogas in the future was inevitably metabolized by these acid consumers. To avoid excessive biomass loss during pH reduction, the effect of different pH endpoints neutralized by LD on the cumulative/daily methane production has been compared in BMP tests with lime-treated CS and pH 7.6 was finally chosen as the endpoint for LDN (Supplementary Data Figure S5).
## 2 Model Construction
### State Space  
In urban intersections, the state refers to environmental variables that influence the agent's decision-making process. Roadside equipment can use V2X technologies to obtain real-time vehicle information and make driving decisions based on the vehicle's status and surrounding traffic environment. In this study, the state space designed for the agent includes information on vehicle position, speed, and acceleration. The Discrete Traffic State Encoding method is used to discretize the state space, thereby reducing its complexity and enhancing the expressiveness of the state information. The state space covers the approach lanes within 150 m of the stop line, divided into 5 m intervals, resulting in a state space matrix of size 3 \(\times\) 19 \(\times\) 30. To represent position information, elements 0 and 1 are used to indicate whether a vehicle is present at that position. When applying Min-Max  
Figure 1: Agent-based intersection signal control framework.  
normalization to vehicle speed and acceleration information, the calculation formula is as follows:  
(1) Normalization of vehicle instantaneous speed  
\[V_{nor}=\frac{V-V_{min}}{V_{max}-V_{min}} \tag{1}\]  
where \(V_{nor}\) represents the instantaneous normalized speed, which ranges from 0 to 1; \(V\) is the instantaneous speed; \(V_{min}\) is the minimum speed, set to 0.0 m/s; and \(V_{max}\) is the maximum speed, determined by the road speed limit \(V_{lin}\) and an overspeed factor \(V_{f}\). The speed limit is set at 50 km/h. Considering that speeding occurs in real traffic conditions, a speeding factor of 1.2 was introduced so that the maximum speed is 60 km/h. The formula is as follows:  
\[V_{max}=V_{lin}\cdot V_{f} \tag{2}\]  
(2) Normalization of vehicle instantaneous acceleration  
1 When vehicle acceleration is positive, the formula is as follows:  
\[Acc_{nor}=\frac{Acc-Acc_{min}}{Acc_{max}-Acc_{min}} \tag{3}\]  
where \(Acc_{nor}\) represents the instantaneous normalized acceleration; \(Acc\) represents the instantaneous acceleration; \(Acc_{min}\) represents the minimum acceleration, set to 0.0 m/s\({}^{2}\); and \(Acc_{max}\) represents the maximum acceleration, set to 2.6 m/s\({}^{2}\).  
2 When the vehicle acceleration is negative, the formula is as follows:  
\[Dec_{nor}=\frac{Dec-Dec_{min}}{Dec_{max}-Dec_{min}} \tag{4}\]  
Here, \(Dec_{nor}\) represents the instantaneous normalized deceleration; \(Dec\) represents the instantaneous deceleration; \(Dec_{min}\) represents the minimum deceleration, set to 0.0 m/s\({}^{2}\); \(Dec_{max}\) represents the maximum deceleration, set to \(-\)9.0 m/s\({}^{2}\).  
Taking the state space of the north entrance road as an example, the position information matrix corresponds to vehicles A and B at 0 m and 15 m from the stop line, and the processed velocity and acceleration information is entered into the matrix as illustrated in Figure 2.
# Data-Driven Construction of User Utility Functions from Radio Connection Traces in LTE
## 4 Estimation of QoS Thresholds on a Per-Service Basis
### Step 1: Classification of Connection Traces
To this end, the following traffic descriptors are collected per connection:  
* The RRC connection time;
* The total DL traffic volume at the packet data converge protocol level;
* The UL traffic volume ratio \(\eta_{UL}\) [%], computed as \[\eta_{UL}=100\times\frac{V_{UL}}{V_{UL}+V_{DL}}\ ;\] (4)
* The DL traffic volume ratio transmitted in last TTIs, \(\eta_{UL}^{lastTTI}\), computed as \[\eta_{UL}^{lastTTI}=\frac{V_{DL}^{lastTTI}}{V_{DL}}\ ;\] (5)
* The DL activity ratio, \(\eta_{DL}^{active}\), computed as the ratio between active TTIs and the effective duration of the connection, \[\eta_{DL}^{active}=\frac{T_{DL}^{active}}{T_{eff}}\ ;\] (6)
* The session DL throughput, \(TH_{DL}^{session}\) (in bps). Then, burst level parameters required for traffic classification are estimated for each connection from the set of traffic descriptors listed above. From these parameters, connections are divided into groups by hierarchical clustering. Finally, the resulting groups are associated with broad application groups by analyzing the median value of traffic descriptors for connections in each group.
# Development of \"Quadrello di Ovino\", a Novel Fresh Ewe's Cheese
## 2 Materials and Methods  
### Milk and Starter Culture  
Raw ewe's whole milk was obtained from several farms and transported daily by a temperature-controlled road tanker to the dairy factory \"II Cacio Siciliano\" located in Belmonte Mezzagno (Italy). Bulk milk (500 L) was kept refrigerated (4-6 \({}^{\circ}\)C) under stirring until cheese making. Pasteurization occurred at 71 \({}^{\circ}\)C for 10 s in a PS15351 COMAT pasteurizer (CO.MAT. s.r.l., Bellizzi, Italy). After heat treatment, the milk was cooled at 38 \({}^{\circ}\)C to perform starter inoculation. The characteristics of pasteurized milk (average data of the bulks used in this study) were: pH 6.60 \(\pm\) 0.03; somatic cell count 3.12 \(\pm\) 0.13 Log, fat 5.86 \(\pm\) 0.25%; protein 4.99 \(\pm\) 0.15%; casein 3.87\(\pm\) 0.31%; and lactose 3.81 \(\pm\) 0.35%.  
Two different freeze-dried starter formulations CR/A (SF1) and LYOBAC-D CRM (SF2) were purchased from microMilk s.r.l. (Cremosano, Italy) and Alce International s.r.l. (Quistello, Italy), respectively. Both commercial starter cultures were prepared from defined strains of _S. thermophilus_ (SF1: strains CR/A1 and CR/A2; SF2: strains CRM4 and CRM6). Freeze-dried cells (5 Units) were re-activated for 10 min in 2 L of pasteurized ewe's milk, kept under manual agitation, to prepare starter inoculum
## 3 Experimental Setup and Testing
### Setup for Static Temperature Tests  
Figure 5 shows the three specimen geometries selected for the tests at warm temperatures: a standard tensile specimen (RB) for uniaxial tests, a round notched bar with a notch radius of 2 mm (RN), and a plane strain geometry (PS) for multiaxial tests. The specimens' heads are threaded (M12) for them to be mounted in the proper custom high-temperature grips of the testing equipment.  
All tests are executed using a Material Testing System (MTS) Criterion 45.105 universal machine, equipped with a two-zone MTS 684 furnace capable of raising sample temperatures up to 1000 \({}^{\circ}\)C. The test chamber of the furnace has a volume of 50 \(\times\) 50 \(\times\) 50 mm\({}^{2}\). The furnace is controlled by an MTS 409.83, while specimen temperature is measured by two high-temperature thermocouples, fixed to the specimen itself. Axial gauge section elongation is measured by a high-temperature MTS extensometer with ceramic legs and a base length of 25 mm, which can be put in contact with the specimen through a dedicated fence placed in the furnace. Figure 6 illustrates some details of the experimental facility.  
Figure 5: Specim geometries: (**a**) round bar; (**b**) round notched bar; (**c**) plane strain.  
Figure 6: Experimental setup. (**a**) MTS testing equipment for high-temperature tests. (**b**) Details of specimens, fixtures, thermocouples and extensometer.  
The MTS TW Elite test software is used for test execution and data acquisition. The tests are conducted with control of displacement, and with constant speeds of 1 mm/s (RB), 0.25 mm/s (RN2) and 0.5 mm/s (PS), which are conditions that could be regarded as quasi-static. During runs, data from the load cell, displacement transducer, extensometer and thermocouples are acquired. Two temperatures are tested, based on results from the literature: 700 \({}^{\circ}\)C, which falls in the middle of the typical warm temperature range (600-800 \({}^{\circ}\)C) for steels, and 600 \({}^{\circ}\)C, which is regarded by some authors [44] as the best choice for warm forming in terms of cost/benefits. Some preliminary tests were executed at a temperature of 300 \({}^{\circ}\)C. In this case, no appreciable differences were found from tests executed at room temperature.
## 3 Experimental Setup
### Software Implementation
#### 3.2.5 Integration of Prediction and Management  
The software framework is fully integrated with the hardware components, enabling autonomous energy management. The MC-LSTM prediction model drives energy optimization decisions, triggering automated actions, such as discharging external energy sources when necessary. This seamless integration ensures that the system operates autonomously, maintaining optimal energy efficiency and grid stability without the need for constant manual intervention.
# Towards Federated Learning with Byzantine-Robust Client Weighting
## 2 Materials and Methods
### Preprocessing Client-Declared Sample Sizes  
#### 2.2.1 Preliminaries  
The following assumption is common among works on Byzantine robustness:  
**Assumption 1** (Bounded Byzantine proportion).: _The proportion of clients who are Byzantine is bounded by some constant \(\alpha\); i.e., \(\frac{1}{k}|B|\leq\alpha\)._  
The next assumption is a natural generalization when considering unbalancedness:  
**Assumption 2** (Bounded Byzantine weight proportion).: _The proportion between the combined weight of Byzantine clients and the total weight is bounded by some constant \(\alpha^{*}\); i.e., \(\frac{1}{n}\sum_{b\in B}n_{b}\leq\alpha^{*}\)._  
Previous works on robust aggregation [6; 7; 8; 9; 15] either used Assumption 1, without considering the unbalancedness of the data, or implicitly used Assumption 2. However, we observe that Assumption 2 is unattainable in practice since Byzantine clients can often influence their own weight.  
We address this gap with the following definition and an appropriate _Preprocess_ procedure.  
**Definition 1** (mwp).: _Given a proportion \(p\) and a weights vector \(\mathbf{V}=(v_{1},...,v_{|\mathbf{V}|})\) sorted in decreasing order, the maximal weight proportion, \(\operatorname{mwp}(\mathbf{V},p)\), is the maximum combined weight for any \(p\)-proportion of the values of \(\mathbf{V}\):_  
\[\operatorname{mwp}(\mathbf{V},p)\coloneqq\frac{\sum_{i=1}^{p}v_{i}}{\sum_{i=1}^{ |\mathbf{V}|}v_{i}}\enspace\text{,}\quad\text{where}\quad t=\lfloor p|\mathbf{V}|\rfloor.\]  
_Note that this is just the weight proportion of the \(\lfloor p|\mathbf{V}|\rfloor\) clients with the largest sample sizes._In the rest of this work we assume Assumption 1 and design a _Preprocess_ procedure that ensures the following:  
\[\text{mwp}(Preprocess(\mathbf{N}),\alpha)\leq\alpha^{*}. \tag{3}\]  
Observe that this requirement enables the use of weighted robust mean estimators in a realistic setting by ensuring that Assumption 2 holds for the preprocessed client sample sizes. It should also be noted that here, \(\alpha\) is our assumption about the proportion of Byzantine clients, whereas \(\alpha^{*}\) relates to an analytical property of the underlying robust algorithm. For example, we may replace the federated average with a weighted median as suggested by [PERSON] et al. [11], in which case, \(\alpha^{*}\) must be less than \(1/2\).
## 2 Materials and Methods  
### Data Preprocessing  
The input matrix X (\(g\times n\)) is a count matrix with g genes and n cells. Firstly, the genes with a mean expression of less than 0.001 and non-zero expression of fewer than three cells were removed because of the high dropout rate of scRNA-seq data. Secondly, we normalized the matrix X by the library size of each cell to eliminate the effect of sequencing depth; this standardized approach is called CPM. After CPM normalization, some data were very large and some were very small. Therefore, the log transformation with pseudo-count 1.01 was then performed to prevent subsequent processing from being affected by the large values. Specifically, we define the normalized and log-transformed matrix as \(Y=\left(y_{ij}\right)_{g\times n}\), and we set  
\[y_{ij}=log_{10}\left(\frac{x_{ij}}{\sum_{i=1}^{3}x_{ij}}\times 1,000,000+1.01 \right)\,,\;i=1,2,\ldots,g,j=1,2,\ldots,n \tag{1}\]  
Figure 1: The outline of scCGImpute. scCGImpute takes the scRNA-seq data as input. The output is the imputation that leverages the information between cells and genes simultaneously. The blue, green and cyan colors in the figure represent different cell types. In the matrices, the darkness of colors represents the level of gene expression, while in the gene correlation matrix, the darkness of colors represents the level of correlation.
## 4 Evaluation Metrics and Validation Scheme
### Validation Scheme  
In this work, we built a CNN model with three input layers in which the raw data was used as the input data of the first layer. Spectrum data and envelope data were selected and used as input data of the second and third layers, respectively. We chose two types of rolling bearing datasets for our experiment to test the validity of the proposed MDI-CNN model. In addition, in order to verify model robustness, we employed a method that mixed the original signal with the Gaussian noise. The flowchart of the validation method is presented in Figure 2. The general process can be summarized as follows.  
1. The original signal and the noisy signal that was obtained by mixing the original signal with Gaussian white noise, were processed by the fast Fourier transformation (FFT) and envelope spectrum analysis (ESA) to form the dataset A and dataset B, respectively. 87.5% of a sample in a randomly selected dataset consists of a training set, and the remaining samples are composed of a test set.
2. The network parameters, including \(w\), \(b\), number of iterations _numepochs_, number of batch processing _batchsize_, learning rate _alpha_, and weight coefficient \(q\), were initialized.
3. The training set A was entered into the CNN model in batches for adaptive extraction of signal characteristics. The error between the predicted and actual values after the forward propagation was calculated. The error was then propagated in reverse direction by the BP algorithm in order to update the network parameters A and B layer by layer until the end of the iteration.
4. Test datasets A and B were entered into the trained MDI-CNN model to verify the validity and robustness of the model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Actual Value**} & \multicolumn{2}{c}{**Predicted Value**} \\ \cline{2-3}  & **Label 1** & **Label 2** \\ \hline Label 1 & TP & FP \\ Label 2 & FN & TN \\ \hline \hline \end{tabular}
\end{table}
Table 1: The model of the confusion matrix.  
5. The training set B was fed into the trained MDI-CNN model for incremental training. Finally, generalization performance was validated by entering test dataset B again into the model.
# Accurate Indoor Localization with IoT Devices and Advanced Fingerprinting Methods
## 3 Results  
### WCL Location Results  
As shown in Figure 7, The results of this algorithm, which is actually implemented without any changes or improvements, are not very satisfactory and the error rate in some places reaches more than 4 m.  
If we consider the location of each transmitter as three vertices of a triangle, the points at which the lowest error rates are recorded are the points closest to the center of this triangle. But many other errors have been observed. The average error in this method is 2.51 m. The error variance in this method is 0.92 and the standard deviation is 0.96.
## 2 Defect Detection Technique Using Multi-Channel Eddy Current System
### 16 Ch Eddy Current Sensors  
Plus-point sensors, which are a type of differential sensors, were used in this study [11]. The plus-point sensors crossed the \"I\"-shaped coils crosswise to differentially detect the defects; as the lift-off (the gap between the specimen and the sensor) changes, the crossed coils are simultaneously affected and canceled, thus minimizing the effect of lift-off and reducing noise [12; 13]. Furthermore, defects in various directions rather than in one direction can be detected [14]. The plus-point sensors were fabricated with four layers (two layers each) with an outer diameter of 8 mm and a height of 30 mm.  
The jig was constructed as shown in Figure 2a,b such that the 16 sensors could be secured and the entire rail surface could be inspected. The sensor spacing in the direction of rail propagation was set to 18 mm, and to inspect the entire rail head, the sensor spacing between the rail propagation direction and vertical direction was 3.8 mm, which resulted in a total of 57 mm. The jig to which the sensors were connected was divided into four parts and could be disassembled and reassembled. Four sensors were combined onto one jig apparatus, and the arrangement of the sensors could be changed by disassembling and reassembling the jig. Furthermore, a constant lift-off distance from the wheels attached to the jig to the rail surface could be maintained.