esa-sceva/satcom-chunk-collection huggingface dataset:
# Emotion Recognition on Call Center Voice Data
###### Abstract
As a result, any improvement in this area will positively affect the company's customer experience. The emotion analysis system will make the job of the call center employee easier and increase their performance. The paper's novelty lies in our study of speech emotion recognition using call center speech data, specifically real-life customer data provided by \"Turkcell Global Bilgi Pazarlamas Damsmalk ve Cagr Servisi Hizmetleri Inc.\" We focused on recognizing speech emotion based on a hybrid model, which utilizes text and voice attributes. The prevailing theme in the literature on speech emotion recognition is a focus on examining the voice qualities of speech and speakers. Furthermore, the datasets utilized by researchers have predominantly been captured in a controlled studio setting and performed by skilled voice actors. Nevertheless, in actuality, individuals convey their emotions through speech using more authentic intonations and less proficient language. For instance, an individual who is experiencing fear or confusion should not be expected to articulate with accuracy in terms of diction and language. This has the potential to result in the failure of the proposed models that were previously used on actual speech data. Upon analyzing the literature pertaining to emotion recognition in text, it becomes evident that the majority of studies focus on analyzing corpora and lexicons. Experts have meticulously crafted and annotated the corpora and lexicons. Utilizing these works in actual spoken language data can also result in failure. The dataset used in the article was obtained from actual call center calls in the Turkish language. It consisted of a training set containing 520,000 original sentences and a test set containing 48,000 sentences. The allegorical structure of the Turkish language and the fact that many different words can be used for a single emotion pose a challenging structure for the emotion recognition system. For example, there are 84 different words in the Turkish language that mean sadness, and each of these words indicates differences in the level of sadness a person can have. Additionally, we annotated the data using [PERSON]'s emotional labels. The uniqueness and importance of this study lie in the utilization of authentic human voice data, specifically obtained from call center conversations involving consumers of the Turkish mobile operator Turkcell. This feature sets our work apart in the field of emotion recognition, namely, in the domains of speech and text analysis. Furthermore, the utilization of the speech-to-text technique we employ poses a challenge to the identification of emotions based on both vocal and textual aspects, hence enhancing the significance of our work. The rest of the paper is organized as follows: In Section 2, we discuss text-based and voice-based emotion recognition works in the literature. Later on, the methodology of our study and dataset attributes are given in Section 3. Finally, the results and discussion are presented in Section 4.
## 3 Design of Hybrid Control Scheme
### Design Method of \(Q(s)\)  
The design and optimization of \(Q(s)\) in Figure 7 is not easy when the requirements of performance and robustness must be considered together, especially when there are some performance demands. A systematic method in dealing with this issue was proposed in [32]. The block diagram transformation for Figure 7 yields Figure 10a. In order to use robust control theory, we take  
\[Q(s)=\frac{P_{\rm n}(s)K(s)}{1+P_{\rm n}(s)K(s)} \tag{21}\]  
where the internal-loop compensator \(K(s)\) is stable. Then the structure of the hybrid suppression scheme can be transformed equivalently to a structure in Figure 10b. In this way, the design of \(Q(s)\) can be transformed to the design of \(K(s)\).  
In Figure 10, \(K(s)\) is the robust internal-loop feedback controller. If \(K(s)\) is designed for the nominal model \(P_{\rm n}(s)\) in order to satisfy a given performance and robustness criterion, the optimal \(Q(s)\) of the DOB can be systematically designed by the optimal method under the given specific conditions, because the transfer function of the unity feedback system with \(P_{\rm n}(s)\) and \(K(s)\) can determine \(Q(s)\) in Equation (21).  
The H\(\alpha\) mixed sensitivity method is utilized to derive the optimal robust internal loop compensator \(K(s)\), and then the optimal \(Q(s)\) can be derived by Equation (21). The sensitivity function \(S_{\rm Q}\) and the complementary sensitivity function \(T_{\rm Q}\) of \(Q(s)\) are obtained from Equation (22), respectively  
\[S_{\rm Q}=\frac{1}{1+P_{\rm n}(s)K(s)},\;T_{\rm Q}=\frac{P_{\rm n}(s)K(s)}{1+P _{\rm n}(s)K(s)} \tag{22}\]  
Apparently, \(S_{\rm Q}=1-\;Q\) and \(T_{\rm Q}=Q\). Then the H\(\alpha\) mixed sensitivity problem can be described as solving \(K(s)\) to meet the minimum H\(\alpha\) norm  
\[\min_{K_{\rm stability}}\left|\begin{matrix}W_{1}(1-Q)\\ W_{3}Q\end{matrix}\right|_{\infty}<1 \tag{23}\]  
Figure 10: Equivalent structure of DOB: (**a**) from diagram transformation; (**b**) by definition.  
Then, this provides a simple way to do trade-offs. In light of this, the characteristics of \(Q(s)\) can be planned through the design of weighting functions, and they make it easy to consider the middle and high frequency part of \(Q(s)\), which can guarantee the robustness, but the model error is amplified in some frequency bands, which restricts the closed-loop bandwidth.
## 4 The Proposed Routing Algorithm
### Packet Delivery Predictability  
In the network model, edge nodes are deployed at certain locations. Suppose that edge node \(v\) is located at a certain location in the area. The probability that a mobile user visits edge node \(v\) from time slot \(t+1\) to time slot \(t+k\) is obtained by using the human location prediction model. Let \(p(u,v)_{t+j}\) denote the probability that mobile user \(u\) visits edge node \(v\) at time slot \(t+j\). Based on the visit probability, packet delivery predictability (the possibility that a node will deliver a packet to its destination) can be calculated. Let \(
# Image-Based Gimbal Control in a Drone for Centering Photovoltaic Modules in a Thermal Image +
## 2 PV Module Imaging System Using Drone Equipped with Dual Camera
### Design of GCS  
The block diagram of our drone system is shown in Figure 6. The proposed thermal imaging system captures the visible and thermal images of the PV modules and uses the visible image to derive the angle and center position of the PV module area. The angle of the derived module area is converted into the adjustment value for controlling the horizontal angle, and the position of the central area is converted into the adjustment value for controlling the vertical angle. The obtained adjustment values are inputted to the gimbal mechanism by calculating it with the signal of the wireless communication unit for the external wireless device.  
Figure 5: Gimbal control system (GCS) equipped with dual camera.  
Figure 6: Block diagram of photovoltaic module imaging system using drones equipped with thermal and visible cameras.  
The gimbal control method is divided into vertical control and horizontal control. Firstly, it is necessary to find the center position of the PV module through the gimbal's vertical adjustment. If the PV module does not exist at the center of the thermal image, the thermal image with the upper and lower parts of the PV module will be out of the ROI. In this case, it is difficult to detect the hotspot area of the PV module accurately. Secondly, after having the right vertical respect adjustment, the gimbal needs to be aligned horizontally to adjust the gimbal tilt so that the PV modules in the image can be captured in the proper horizontal orientation. Although we explained the process in two steps, it works simultaneously. Therefore, the camera needs to be adjusted. Drone images are used to find the angle of the camera and the center of each module. With the horizontal and vertical angle control function, the gimbal is controlled in real-time so that the visible region and the thermal image are horizontal, and the ROI is captured at the center of the image.
# Hierarchical Transmission Tower Detection from High-Resolution SAR Image
## 2 Methodology
### Candidate Transmission Tower Pixels Detection  
According to the transmission tower characteristic (2) in Section 2.1, the spatial density of potential transmission tower pixels is higher than the spatial density of pixels in the background area. Therefore, candidate transmission tower pixels can be further filtered by calculating spatial density of the potential transmission tower pixel and based on a density threshold. Constructing a square window \(W_{i}\) centered at potential transmission tower pixel \(i\), and the spatial density of the \(\mathbf{W}_{i}\) can be expressed as  
\[\Delta_{i}=\#\bigl{\{}i^{\prime},i^{\prime}\in\mathbf{P},(x_{i^{\prime}},y_{i^{ \prime}})\in\mathbf{W}_{i}\bigr{\}} \tag{4}\]  
for all potential transmission tower pixels, their spatial density set is \(\mathbf{\rho}=\{\Delta_{i},i^{\prime}\in\mathbf{P}\}\), where \(\#\) denotes the operation of counting the number of elements in a set.  
As shown in Figure 3, the number of pixels contained in different windows varies. By calculating the pixel spatial density, the windows with higher density are detected by combining the predefined density threshold \(T_{\Delta}\) to obtain the set of candidate transmission tower pixels. Taking the threshold \(T_{\Delta}=16\) as an example, Figure 3 shows the calculation of the pixel spatial density for candidate transmission tower pixels, in which the interfering pixels in the two green square windows will be filtered out. Therefore, the index set of candidate transmission tower pixels can be expressed as \(\mathbf{C}=\{i,\Delta_{i}\geq T_{\Delta},i\in\mathbf{P}\}\). By statistical analysis of the image, \(T_{\Delta}\) can be obtained as follows:  
\[T_{\Delta}=\left\lfloor\frac{2n_{\mathbf{W}_{i}}}{3}\right\rfloor \tag{5}\]  
where \(\lfloor\cdot\rfloor\) is the downward rounding symbol, \(n_{\mathbf{W}_{i}}\) represents the total number of pixels in the window \(\mathbf{W}_{i}\). The reason is that if they are transmission tower pixels, they should dominate within the \(n_{\mathbf{W}_{i}}\) and correspond to a higher number of pixels, otherwise, smaller than \(T_{\Delta}\) are background pixels.  
Figure 2: SCR histogram. (**a**) Single-peak distribution. (**b**) Multi-peak distribution.
## 4 Results
From Table 1, one can see that all small- and medium-sized instances were solved in between a few seconds and six minutes. Meanwhile, most of the large-sized instances from Table 2 reached the maximum computational time of 1 h. It is worth remembering that instances of sizes of up to 500 containers are prohibitive for exact approaches since this generates an exorbitant number of variables, but they can be successfully solved by a personal computer when using the proposed approach. In other approaches from the literature, such as [34; 41], the biggest instances tested were of up to 10,000 containers and for a single CRP. In [10], the heuristic approach proposed was tested in instances of up to 700 containers, and the authors of [46] solved a single CRP with 45 containers. Figures 11 and 12 illustrate the evolution of the objective function of each instance (i.e., the total number of relocation movements of the best individual at each generation). Note that in a few instances, the GA could not improve the best solution of the first generation, but most instances had great improvements in the first generations. Nevertheless, the most important point is that good-quality solutions were obtained for the large-sized instances in low computational times, showing that the proposed approach can be applied in real case scenarios or even have commercial applications. Note that in all instances, as the occupation of the yards increased, the computational time required to solve the problem also increased. When comparing the three types of transportation matrices, observe that the size required for the ship also depended on the type of matrix. When the containers stayed on the ship for more ports before being unloaded, more space was required to carry all the containers. When the containers stayed  
Figure 11: Evolution of the objective function of small- and medium-sized instances. Figure 12: Evolution of the objective function of large-sized instances. on the ship for only a few ports before being unloaded, less space was required. Due to these types of differences in the test instances, it is important to have a good variety of rules to be simulated, as we proposed. Additionally, note that for the short-distance instances (type \(M=2\)), less time was required to finish the optimization, and fewer relocations were performed in most cases. Meanwhile, the long- and mixed-distance instances (types \(M=3\) and \(M=1\), respectively) took more computational time and relocations per instance. These results show that the integration of the CRP with the ship MPSP must be considered to avoid a misleading analysis and unexpected extra relocation movements.
# Reducing the Number of Sensors in the Data Glove for Recognition of Static Hand Gestures
## 1 Introduction
To the best of our knowledge, no research has yet been conducted on the feasibility of reducing the number of detectors in structures of similar purpose, and no research with publicly available results has been conducted on the Polish Sign Language alphabet using 10 flex sensors. Reducing the number of sensors, and thus the size of the feature vector, will simplify not only the construction of the glove itself but also the classification process. A simpler design and a reduction in the necessary computational power could improve the usability of such a data glove. Furthermore, less complex data gloves that can be used as sign-language-to-speech translators may be less expensive and may be more widely accepted.
# Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction
## 4 Experimental Results
### Double-Step Architecture Comparison  
In this section, a comparison of the four proposed architectures is performed: DS-UNet, DS-UNet++, DS-SegU, and DS-AttU. For simplicity, the comparisons in the binary context are performed by considering only the BCE loss, while no clear winner can be identified within the severity estimation context.  
In the burned areas delineation problem (Table 1), the DS-UNet and DS-UNet++ models were demonstrated to be the best performing architectures in terms of IoU, followed by DS-AttU model. Instead, SegNet-based UNet achieved the worst performance with the BCE loss.  
Subsequently, analyzing the regression problem and the resulting average value of rMSE obtained by the four considered architectures (Table 2), it is possible to observe that no architecture outperforms the others, independently from the loss functions used for the training process. An exception is the DS-SegU model, which always performs the worst or second-worst with every considered loss configuration. Computing the mean and median rankings on the rMSE scores achieved by the three remaining architectures, we obtained the following:  
* DS-UNet: 1.857 (mean), 2 (median);
* DS-UNet++: 1.857 (mean), 2 (median);
* DS-AttU: 2.429 (mean), 2 (median);  
Consequently, no clear advantage of one architecture concerning the others can be observed as the loss functions changes, especially between DS-UNet and DS-UNet++ architectures. According to rMSE metric, the four most performant configurations are DS-UNet with Dice-MSE, BCE-MSE, and BCE-MSE\(\cdot\)F\({}_{1}\), followed by DS-AttU with BCE-MSE\(\cdot\)F\({}_{1}\) configuration. All the mentioned models with the corresponding loss functions  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Model** & **BCE** & **Dice** & **B + D** & **B + S** & **BCE** & **sIoU** & **sIoU** \\  & **MSE** & **MSE** & **MSE** & **MSE** & **MSE\(\cdot\)F\({}_{1}\)** & **sIoU** & **MSE** \\ \hline DS-UNet & 1.32 & **1.30** & 1.42 & 1.83 & 1.33 & 2.26 & 1.73 \\ DS-UNet++ & 1.41 & 1.41 & **1.39** & 1.55 & 1.40 & 2.23 & 1.61 \\ DS-SegU & **1.66** & 1.82 & 1.88 & 2.05 & 1.79 & 2.97 & 2.17 \\ DS-AttU & 1.38 & 1.47 & 1.41 & 1.45 & **1.35** & 2.62 & 2.18 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performances in terms of rMSE. Results on burnt-areas only, with different loss functions, for the final regression backbone. The best loss configuration for each model is highlighted in bold.  
achieved an rMSE score lower than 1.5. Thus, the DSF achieves good-quality results over the entire dataset, making in general mistakes from one class to neighboring ones, considering the severity level, e.g., a completely destroyed pixel may be classified as severely damaged, or vice versa. Such a result could provide beneficial information to first responders and an initial estimation of damage severity through remote sensing for recently extinguished wildfires.
# PSDFH: A Phase-Space-Based Depth from Hologram Extraction Method
## 3 Proposed Methodology
### Step 1: Computing the Phase Space Representation of the Hologram  
We assume that a 1D hologram of a 2D scene has been generated. Given the 1D input hologram denoted \(\mathcal{H}\in\mathbb{C}^{\text{W}}\), its 2D phase space transform is computed using the STFT based on sub-sampled hologram points \(x\) and sampled frequencies \(\xi\) defined as  
\[\bigg{\{}x[n_{1}]:=\frac{W\Delta_{x}}{2N}(2n_{1}-N+1):n_{1}=0,1,\ldots,N-1 \bigg{\}}, \tag{5}\]  
\[\bigg{\{}\xi[m_{1}]:=\frac{1}{2\Delta_{x}M}(2m_{1}-M+1):m_{1}=0,1,\ldots,M-1 \bigg{\}}, \tag{6}\]  
where \(W\) is the hologram size, \(\Delta_{x}\) is the hologram pixel pitch, and \(N\) and \(M\) are the number of sampled points and frequencies.The 2D phase space representation of the hologram is therefore given by  
\[L[n_{1},m_{1}]=\sum_{s=0}^{L-1}w[n_{1}-s]H[s]e^{-js[m_{1}]}, \tag{7}\]  
where \(w\) is a Hann window of size \(M+1\) defined as  
\[w(m)=0.5(1-\cos(2\pi\frac{m}{M})),\quad 0\leq m\leq M+1. \tag{8}\]  
For a colored hologram, the STFT transform is applied to each color channel \((R,G,B)\) of the hologram producing a complex-valued tensor of size \(N\times M\times 3\).
## 5 Application Case
### Geological and Engineering Background
#### 5.1.2 Engineering Background  
Mining has been taking place in the Yanqianshan Iron Mine for over fifty years. According to the location of the ore body and the mining time, the Yanqianshan Iron Mine can be divided into three stages. The first stage is open-pit mining, which started in 1960 and ended in 2012. After open-pit mining, the size of the mine pit formed was large; since 2012, transitional ore body mining has been carried out, and the main ore bodies of the mine are on the west, east, and north sides. The ore body on the north side of the pit is underground mined for the ore body below the bottom of the pit after the end of the transition period, and the mining depth will not exceed \(-\)500 m.
## 3 Materials and Methodologies
### Resampling Model
#### 3.4.1 Historical (Past-Present)  
Let the historical setting of the input and output matrix be \((X^{t},Y^{t})\) (\(t=1,...,t\)), where t = 1 is the first period and t = t is the last period with \(X^{t}=(x_{1}^{t},\ldots,x_{n}^{t})\) and \(Y^{t}=(y_{1}^{t},\ldots,y_{n}^{t})\). The number of the DMU is \(n\), and \(x_{j}^{t}\in R^{m}\) and \(y_{j}^{t}\in R^{s}\) are the input and output vectors of \(\text{DMU}_{j}\), respectively. In terms of the choice of a weighting scheme for historical data, we used the Lucas model to calculate the weight information of the past and the present [9] by setting the weight \(w_{t}\) to period \(t\) and assuming the weight is rising in \(t\). For this aim, the following Lucas number series (\(l_{1},...,l_{T}\)) (a variant of fibonacci series) was a candidate where we had  
\[l_{t+2}=l_{t}+l_{t+1}(t=1,\ldots,T,T-2;l_{1}=1,l_{2}=2) \tag{5}\]  
Let the sum be \(L=\sum_{t=1}^{T}lt\), and weight \(w_{t}\) is defined by  
\[w_{t}=l_{t}/L(t=1,\ldots,T). \tag{6}\]  
In this research, we let \(t=5\), we had \(w_{1}=0.0526\), \(w_{2}=0.1053\), \(w_{3}=0.1579\), \(w_{4}=0.2632\), and \(w_{5}=0.4211\). Thus, the influence of the past period disappeared gradually.  
Super-efficiency scores: applied historical data (\(X^{t},Y^{t}\)measure) (\(t=1,...,t\)) to gauge the confidence interval (CI) of the last period's scores. First, the super-efficiency scores of the last period's DMUs were evaluated [9]. Then, the CI of DMU was measured using the replicas from \((X^{t},Y^{t})\) (\(t=1,...,t\)) as follows.  
Choice of the replication process and the number of replicas: The historical data \((X^{t},Y^{t})\) (\(t=1,...,t\)) were regarded as discrete events with probability \(w_{t}\) and cumulative probability:  
\[W_{t}=\sum_{i=1}^{t}w_{i}(t=1,\ldots,T) \tag{7}\]  
The process of replication based on bootstrapping was first introduced by Efron [30]. Bootstrapping refers to a collection of methods that randomly resample and replace the original sample. In the study, by applying a variant of Bayesian bootstrapping, the weighting scheme is composed of the Lucas number series-based weights \(w_{t}\) because it is more suitable for resampling over a past-present time frame and considering the latest information will become more valuable. For a non-correlated and homoscedastic dataset, we can summarize the data generation process (DGP) in this study as follows. First, a random number of 5 is drawn from the uniform distribution over the interval [0,1], no matter which cross-sectional data (\(X^{t},Y^{t}\)) are used in order to resample \(W_{t-1}<\zeta<W_{t}\), where \(W_{0}=0\). Repeat this process as many times as essential to generate the required number of the valid replicas.
# Leveraging Blockchain Technology for Secure 5G Offloading Processes +
###### Abstract  
This paper presents a secure 5G offloading mechanism leveraging Blockchain technology and Self-Sovereign Identity (SSI). The advent of 5G has significantly enhanced the capabilities of all sectors, enabling innovative applications and improving security and efficiency. However, challenges such as limited infrastructure, signal interference, and high upgrade costs persist. Offloading processes already address these issues but they require more transparency and security. This paper proposes a Blockchain-based marketplace using Hyperledger Fabric to optimize resource allocation and enhance security. This marketplace facilitates the exchange of services and resources among operators, promoting competition and flexibility. Additionally, the paper introduces an SSI-based authentication system to ensure privacy and security during the offloading process. The architecture and components of the marketplace and authentication system are detailed, along with their data models and operations. Performance evaluations indicate that the proposed solutions do not significantly degrade offloading times, making them suitable for everyday applications. As a result, the integration of Blockchain and SSI technologies enhances the security and efficiency of 5G offloading.  
5G; offloading; blockchain; marketplace; SSI; authentication; security +
Footnote †: This article is an expanded version of a paper entitled “An Authentication System Based on Self-sovereign Identity for Vehicle-to-Vehicle (V2V) Communications”, which was presented at the International Congress on Blockchain and Applications, held in Salamanca in June 2024.