esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Microwave Treatment
#### 3.3.2 Effect of Microwave Irradiation on Iron Removal  
Effect of Microwave Irradiation Time on Iron Removal  
Experiments were conducted for different microwave irradiation times (10 to 50 min) at a magnetic intensity of 5000 Gauss, roll speed of 40 rpm, and feed rate of 24 kg/h. The irradiation time results indicate that iron recovery in the non-magnetic concentrate decreased with increasing irradiation time (Figure 11). This decline can be attributed to the transformation of iron-impurity minerals into ferromagnetic minerals, leading to an increase in magnetic susceptibility and magnetic properties as the irradiation time increases. Conversely, the alumina recovery in the non-magnetic concentrate increased with increasing the irradiation time (Figure 11). However, the results indicate that irradiation for >30 min saturated the removal efficiency of iron minerals, reaching approximately 89%. This indicates that microwave treatment for 30 min is optimal for pre-treating Saudi low-grade pyrophyllite ore, a conclusion supported by the findings of [21].  
Figure 11: Effect of microwave irradiation time on Fe\({}_{2}\)O\({}_{3}\) and Al\({}_{2}\)O\({}_{3}\) recovery\(\%\) in non-magnetic fraction.  
In order to study the effect of field intensity on iron removal, samples that underwent 30 min of microwave irradiation were subjected to different magnetic field intensities ranging from 1000 to 5000 Gauss at a roll speed of 40 rpm and feed rate of 24 kg/h. The results show that as the magnetic field intensity increases, the alumina recovery percentage increases in the non-magnetic concentrate. The iron recovery decreased at higher intensities (Figure 12). However, magnetic field intensities exceeding 2000 Gauss saturated the iron removal efficiency, reaching approximately 89%. This may be attributed to the fact that a magnetic field intensity of 2000 Gauss was adequate for removing ferromagnetic minerals from microwave-treated pyrophyllite samples irradiated for 30 min.
# Prospects of Structural Similarity Index for Medical Image Analysis
## 4 SSIM in Medical Imaging
### Magnetic Resonance Imaging
The objective of this research is to decrease the inconsistency of a diagnosis when subjective observers read the medical images. In addition, SSIM contributed significantly to the fusion approach. A unique SSIM implementation in this article was shown because SSIM was not used as the IQA metric; instead, it was utilized as the image fusion itself. Based on an understanding of the image fusion from several imaging modalities, the decision confidence may be improved, and the images combined from MRI and CT were employed to generate more perceivable images. Because CT can offer better information in denser tissue with less distortion and MRI provides adequate imaging for soft tissue, a fusion was executed to reduce the workload of the radiologists. Their results indicated that image fusion using SSIM is remarkable compared with existing image fusion methods, such as a Laplacian pyramid (LP), gradient pyramid (GP), contrast pyramid (CP), steerable pyramid (StrP), and discrete wavelet transform (DWT). For a denoising method to remove Rician noise from MR images, the SSIM and MSE as objective metrics were compared with the MOS to measure the denoising methodology using the discretized total variation [87]. In this study, SSIM performed suitably with the MOS, which is related to HVS. With the highest standard deviation, the SSIM scores diminished following the subjective measurement scores. In addition, in 2013, to reduce the presence of aliasing, an improve compressed sensing technique was introduced, whereas the SSIM and PSNR were the objective measures used to assess the effectiveness of this approach [37]. Their results showed that SSIM is suitable for use with the proposed method as an objective IQM. Subsequently, in 2015 and 2017, to restore the MR images from the existence of noise during the acquisition steps, the SSIM along with SNR, PSNR, MSE, and RMSE was used to evaluate novel denoising algorithms [88; 89; 90; 40]. With the computational improvements, an artificial intelligence (AI) method including deep learning has been proposed to solve the medical image analysis issues, and the results were compared using SSIM [38; 39; 91; 92; 93; 94]. SSIM was also compared with other objective IQAs to obtain comprehensive insight related to an effective IQM for diagnosis by five radiologists [36] and was employed in measuring an image acquisition [95]. To conclude this subsection, Table 1 briefly presents the SSIM applications for MR medical images. In Table 1, SSIM is adopted not only for IQA but also for other purposes, such as image fusion, segmentation, clustering, and loss function. In addition, compared to other metrics, SSIM performances are competitively adequate according to the applied task. Most of the SSIM implementations as an IQA showed that SSIM is suitable for an assigned task, indicating that it offers reliable performance for measuring an enhancement of the image quality in comparison to the other traditional measures.
## 5 Critical Aspects of FSS Technology  
While research into FSS technology has advanced significantly, several limitations remain, including sensitivity to environmental conditions, fabrication tolerances, and challenges in scaling up for complex geometries. The need for adaptable, multi-band, or reconfigurable FSS designs is another area where research gaps exist, particularly as modern telecommunications and sensor systems demand more flexibility.  
FSS technology finds its primary applications in aerospace, defense, telecommunications, and other high-performance industries, but broader adoption, particularly in building design, remains limited due to cost and complexity. Though FSSs could enhance energy efficiency and electromagnetic shielding in buildings, it is better suited to specialized environments, such as data centers and military facilities, rather than general residential structures. Looking ahead, future research will likely focus on the development of reconfigurable FSSs, improved material durability, and multi-functional designs, potentially expanding the technology's applicability and enhancing its real-world performance.
## 2 Materials and Methods
### Sampling of VOCs and SVOCs from Asphalt Mixture Fumes by SPME  
The chemical contents of the asphalt mixture fumes were sampled using the system shown in Figure 1. A side valve on a 500-mL three-neck round-bottom flask containing a magnetic bar was linked to an air pump by a Teflon hose. The central neck was fitted with an exchangeable screw-cap and Teflon septum. The other side valve was connected by a Teflon hose to a Tedlar gas sampling bag containing a sample of fumes. The three-neck round-bottom flask was purged to vacuum using the pump. The valve connected to the fume sample bag was then opened, transferring the fume sample to the evacuated round-bottom flask. The fume was stirred (1500 rpm) using a  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Mixture**} & \multicolumn{4}{c}{**Composition (g)**} & \multicolumn{2}{c}{**Natural**} & **Asphalt** & **RAP** & **Production** \\  & **Natural** & **Asphalt** & **Natural** & **Recalized** & **Aggregates** & **Binder** & **Temperature** & **Temperature** \\  & **Aggregates** & **Binder** & **Exolite** & **Asphalt** & **Temperature** & **Temperature** & **(\$^{\circ}\)C** & **of Mikes** \\  & **Asphalt** & **Added** & **Exolite** & **Pavement (RAP)** & **(\$^{\circ}\)C** & **(\$^{\circ}\)C** & **(\$^{\circ}\)C** \\ \hline P-155 & 4.000 & 216 & 0 & 0 & 155 & 155 & - & 155 \\ Z0.3-135 & 4.000 & 216 & 12 & 0 & 135 & 155 & - & 135 \\ Z0.6-135 & 4.000 & 216 & 24 & 0 & 135 & 155 & - & 135 \\  & & & 400 & & & & & \\ PR10-155 & 3600 & 207.6 & 0 & (10\% of the aggregates) & 142 & 155 & 25 & 155 \\ Z0.6-R10-125 & 3600 & 207.6 & 24 & (10\% of the aggregates) & 142 & 155 & 25 & 125 \\ PR20-155 & 3200 & 199.2 & 0 & (20\% of the aggregates) & 169 & 155 & 25 & 155 \\ Z0.6-R20-135 & 3200 & 199.2 & 24 & (20\% of the aggregates) & 169 & 155 & 25 & 135 \\  & & & & 1200 & & & & \\ PR30-155 & 2800 & 194.4 & 0 & (30\% of the aggregates) & 202 & 155 & 25 & 155 \\ Z0.6-R30-145 & 2800 & 194.4 & 24 & (30\% of the aggregates) & 202 & 155 & 25 & 145 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Raw materials and manufacturing temperatures for asphalt mixtures samples.  
magnetic stirrer bar and heated to 50 \({}^{\circ}\)C to favor convection. Sampling was carried out with a divinylbenzene/polydimethylsiloxane (DVB-PDMS)-coated fiber (65 \(\upmu\)m) and a manual SPME holder (Supelco Analytical, Bellefonte, PA, USA). This fiber is suitable for applications of SPME for non-polar organic compounds, such as benzene, toluene, ethylbenzene and xylenes, and polycyclic aromatic hydrocarbons (PAHs), etc. [68]. The SPME fiber was exposed to the fume sample for 30 min by manual insertion through the central neck septum of the round-bottom flask. Finally, the SPME fiber was inserted into the CG injection port for 1 min at 250 \({}^{\circ}\)C for sample desorption. The process was repeated three times for each fume sample.
## 3 Methodology
### Soil Erodibility Factor-K  
The K-factor represents the typical long-term response of soil to rainfall and runoff. This concept can be further supported by the fact that rainfall detaches soil particles, which are then carried away by runoff. The repeated occurrence of this process over time results in soil erosion, which is numerically estimated using the K-factor. This factor specifies the soil loss for a specific soil type with a unit rainfall erosion index. The USLE model employs [PERSON]'s method to calculate the K-factor, which takes into account various soil characteristics such as soil size, level of organic matter, structure, and porosity of particles [27, 28]. In order to prepare a soil profile for the study area, the FAO digital soil map for the world was utilized. The soil map for the study area was obtained using the \"clip features\" function in ArcGIS Pro 2.8.0. The corresponding K-factors were calculated for each soil type using Equations (3)-(7) proposed by [PERSON] [65]:  
\[K_{\textit{usle}}=f_{\textit{csand}}\times f_{\textit{cl}-\textit{si}}\times f _{\textit{orgc}}\times f_{\textit{hisand}} \tag{3}\]  
\[f_{\textit{csand}}=(0.2+0.3\times\exp(-0.256\times m_{s}(1-\frac{m_{\textit{ silit}}}{100}))) \tag{4}\]  
\[f_{\textit{cl}-\textit{si}}=\left(m_{\textit{sillt}}/(m_{\textit{c}}+m_{ \textit{sillt}})\right)^{0.3} \tag{5}\]  
\[f_{\textit{orgc}}=(1-\frac{0.25\times\textit{orgc}}{\textit{orgc}+\exp(3.72- 2.95\times\textit{orgc})}) \tag{6}\]  
\[f_{\textit{hisand}}=(1-\frac{0.7\times\left(1-\frac{m_{\textit{s}}}{100} \right)}{\left(1-\frac{m_{\textit{s}}}{100}\right)+\exp(-5.51+22.9\times(1-m_{ s}/100))}) \tag{7}\]  
where, \(K_{\textit{usle}}\) represents the factor of erodibility, \(m_{s}\) represents the percent sand, \(m_{\textit{silt}}\) represents the percent silt, \(m_{c}\) represents the percent clay, and \(f_{\textit{orgc}}\) reduces K values in soil with high organic carbon content, \(\textit{orgc}\) shows organic carbon content, \(f_{\textit{csand}}\) is a factor that lowers the K indicator in soils with high coarse-sand content and higher for soil with little sand, \(f_{\textit{cl}-\textit{si}}\) gives low soil erodibility factors for soils with high clay-to-silt ratios, \(f_{\textit{hisand}}\) lowers the K value for soils with extremely high sand content. The corresponding K-factor was computed for each soil type, and the values were entered in the raster table of ArcGIS. By using these values, a K-factor map was generated. The values calculated for each soil type are provided in Table 2.  
The map in Figure 4a,b illustrates the Jhelum watershed's soil type and K factor, respectively; typical K values range from 0.131 to 0.156.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Sr. No.** & **Soil Sample** & **FAO Soil Class** & **K-Factor** \\ \hline
1 & BE & Eutric Cambisols & 0.15 \\ \hline
2 & I & Lithosols & 0.14 \\ \hline
3 & LO & Orthic Luvisols & 0.13 \\ \hline
4 & RC & Calcaric Regosols & 0.16 \\ \hline
5 & GL & Glacier & 1 \\ \hline
6 & WR & Inland water or ocean & 0 \\ \hline \end{tabular}
\end{table}
Table 2: K-factor values for different soil types of the study area.
## 2 Materials and Methods
### High-Impact Weight-Bearing Exercise Program  
The training group performed the HWE program for 50 min a day (10:30 a.m.-11:20 a.m.), three days per week for four months. The training program was composed of 10 min of warm-up, 30 min of HWE, and 10 min of cool-down. The HWE comprised of the clap, walking, jumping burpee, jumping squat, running in place, wall press, bench stepping, jumping lunge, jumping jack, and push up [30]. The exercise intensity including exercise time increased progressively every four week period. The training intensity was set at 60-80% (mean: 136.9 \(\pm\) 9.03 beat/min, range: 123-180 beat/min) of the heart rate reserve (HRR) and monitored every day by using a potable heart rate monitor (Polar RS400, Polar Electro Oy, Kempele, Finland). The detailed training program is shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{2}{c}{**HWE**} & \multicolumn{2}{c}{**CON**} & \multicolumn{2}{c}{**F-Value**} \\ \cline{2-7}  & **Pre** & **Post** & **Pre** & **Post** & **Time** & **Group** & **Interaction** \\ \hline Age (yrs) & 40.3 \(\pm\) 4.23 & - & 40.4 \(\pm\) 3.31 & - & & & \\ Body height (cm) & 160.5 \(\pm\) 4.74 & - & 161.2 \(\pm\) 5.04 & - & & & \\ Body weight (kg) & 58.8 \(\pm\) 10.98 & 57.9 \(\pm\) 9.43 & 62.8 \(\pm\) 11.73 & 63.3 \(\pm\) 12.16 & 0.148 & 1.377 & 1.875 \\ BMI (kg: m\({}^{-2}\)) & 22.7 \(\pm\) 3.62 & 22.3 \(\pm\) 3.14 & 24.1 \(\pm\) 4.14 & 24.3 \(\pm\) 4.46 & 0.277 & 1.409 & 2.137 \\ Total caloric intake & 2151.20 \(\pm\) 487.94 & 2027.71 \(\pm\) 636.44 & 2328.61 \(\pm\) 640.41 & 2274.09 \(\pm\) 582.72 & 0.632 & 1.353 & 0.095 \\ Carbohydrate (g) & 293.29 \(\pm\) 79.16 & 260.95 \(\pm\) 89.44 & 297.46 \(\pm\) 86.66 & 294.98 \(\pm\) 85.55 & 1.409 & 0.497 & 1.036 \\ Fat (g) & 72.75 \(\pm\) 19.01 & 73.82 \(\pm\) 23.36 & 83.69 \(\pm\) 25.82 & 80.00 \(\pm\) 26.35 & 0.065 & 1.495 & 0.215 \\ Protein (g) & 98.08 \(\pm\) 29.89 & 92.35 \(\pm\) 29.89 & 113.30 \(\pm\) 40.46 & 102.07 \(\pm\) 31.60 & 1.665 & 1.498 & 0.175 \\ Vitamin D (ug) & 5.18 \(\pm\) 4.47 & 4.57 \(\pm\) 2.84 & 4.76 \(\pm\) 2.26 & 6.02 \(\pm\) 5.30 & 0.109 & 0.260 & 0.900 \\ Calcium (mg) & 878.26 \(\pm\) 242.49 & 854.96 \(\pm\) 346.25 & 1051.38 \(\pm\) 474.55 & 995.38 \(\pm\) 575.03 & 0.224 & 1.352 & 0.038 \\ Magnesium (mg) & 163.6 \(\pm\) 80.95 & 136.60 \(\pm\) 53.77 & 124.24 \(\pm\) 51.53 & 134.56 \(\pm\) 63.81 & 0.270 & 1.650 & 1.350 \\ \hline \hline \end{tabular}  
* Values are expressed as mean \(\pm\) SD. HWE: high-impact weight-bearing exercise group, CON: control group, BMI: body mass index.  
\end{table}
Table 1: Physical characteristics and nutritional intake of subjects.
# Single Photon Counting Performance and Noise Analysis of CMOS SPAD-Based Image Sensors
## 3 Single Photon Counting Noise Modelling and Analysis  
The first part of this section details a model of read noise and sensitivity (or CVF) developed to characterise our recent work in SPAD-based imaging. The second part discusses three noise measurement methods for DSERN image sensors based on the photon counting histogram (PCH). The use of single photon counting histograms are not new to the imaging community but the analysis presented here seeks to model and quantify the noise measurements that may be obtained from the PCH. A discrete Poisson probability density function (PDF) may represent photoelectrons (or photons) either from multiple reads of a single pixel or a single read of multiple pixels. For a single pixel \"\(i\)\", the PDF for the captured photoelectrons k may be represented as:  
\[P\left(i,k\right)=\frac{\lambda^{k}exp\left(-\lambda\right)}{k!}:k\in\mathbb{Z} \tag{1}\]  
where \(\lambda\) = mean number of photoelectrons in the integration period. PRNU may be modelled to first order as a normal distribution with mean CVF \(\mu_{\text{CVF}}\) and variance \(\sigma_{\text{CVF}}\)2. For each electron \(k\), the ideal voltage domain input signal \(S_{IN}\) is created with the signal from each electrons at a separation \(v_{\left(i,k\right)}\) equal to the CVF for that pixel \"\(i\)\":  
Footnote 2: The \(\sigma_{\text{CVF}}\) is defined as the ratio of the number of electrons in the Poissonian PDF.  
\[v\left(i,k\right)=k\cdot CVF\left(i\right) \tag{2}\]  
\[S_{IN}\left(v_{k,i}\right)=P\left(i,k\right) \tag{3}\]  
For each electron \(k\), assuming the read noise is dominated by thermal noise it follows a Gaussian distribution. Read noise \(\sigma_{RN}\) is applied on each electron's output signal \(S_{k}\) for the range \(v\) = 0 to (\(n\).CVF): where \(n\) is the maximum number of electrons in the Poissonian PDF in Equation (1):  
\[S_{k}\left(v\right)=\frac{1}{\sigma_{RN}\sqrt{2\pi}}\cdot exp\left(-\frac{ \left(v-S_{IN}\left(v_{k}\right)\right)^{2}}{2\sigma_{RN}{}^{2}}\right) \tag{4}\]  
The voltage domain output signal is then represented as the summation of each of the constituent signals for each electron within the PDF:  
\[S_{OUT}\left(v\right)=\sum\limits_{k=0}^{n}S_{k}\left(v\right) \tag{5}\]  
Figure 1 provides a photon counting histogram (PCH) example of the output of the model given by Equation (5) with 10 mV/SPAD event (or 10 mV/e\({}^{-}\) equivalent) and 0.1 e\({}^{-}\) equivalent RN. As seen in the figure, discrete peaks are visible in the PCH. The RN distribution around each photon counting peak can be determined using three recent methods:
# Virtual Source Array-Based Multiple Time-Reversal Focusing
## 2 Review of VSA-Based TR Focusing  
The details of the applications of the VSA concept in TR focusing can be found in Refs. [8; 11; 12]. This section summarizes VSA-based single TR focusing. The phase-conjugated (or time-reversed) field at a field location, \(\vec{r}\), in a frequency domain can be written as  
\[p(\vec{r})=\sum_{m}g^{*}(\vec{r}_{m}|\vec{r}_{s})g(\vec{r}|\vec{r}_{m})=\mathbf{ g}^{\dagger}(\mathbf{r}_{\text{TRM}}|\vec{r}_{s})\mathbf{g}(\vec{r}|\mathbf{r}_{ \text{TRM}}), \tag{1}\]  
where \(g(\vec{r}|\vec{r}_{m})\) denotes the field from the \(m\)th element of the TRM (\(1\leq m\leq M\)) to an arbitrary field location, \(\vec{r}\), and \(()^{*}\) and \(()^{\dagger}\) denote the complex conjugate and Hermitian transpose, respectively. In vector notation, \(\mathbf{g}\) and \(\mathbf{r}_{\text{TRM}}\) are specified as \((M\times 1)\) column vectors. The field for backpropagation, denoted by \(\mathbf{g}(\mathbf{r}_{\text{TRM}}|\vec{r}_{s})\), provides the following synchronized transfer function:  
\[\mathbf{g}(\mathbf{r}_{\text{TRM}}|\vec{r}_{s})\approx\sum_{n}\mathbf{g}(\vec {r}_{n}|\mathbf{r}_{\text{TRM}})e^{-i\omega t_{n}}, \tag{2}\]  
where \(\mathbf{g}(\vec{r}_{n}|\mathbf{r}_{\text{TRM}})\) represents a-priori sampled transfer functions between the TRM and VSA. Prior to backpropagation, the time delay, \(t_{n}\), of the line of sight between the \(n\)th element of the VSA (\(1\leq n\leq N\)) and the selected location, \(\vec{r}_{s}\), is applied to the sampled transfer functions assuming that the field beyond the VSA is a homogeneous medium with constant sound speed, \(c\).
## 3 Proposed Methodology
### Binarized Deep Spiking Capsule Fire Hawk Neural Network Based Packet Classification
BSHNN classifies packets considering three crucial factors: packet success rates, packet loss rates, and packet error rates. A packet that achieves a high success rate with minimal packet loss and low error rates is considered a standard packet within the BSHNN model. Notably, BSHNN employs a dynamic threshold that necessitates variation for different packets. This adaptive approach enables the effective identification of malicious packets solely based on their headers. The packets were classified into two distinct classes, normal and suspicious, based on the data packet features shown in Table 1.  
The criteria that led to prioritizing BSHNN for packet classification over other models include its dual verification process, effective optimization techniques, adaptability to network conditions, application-specific requirements, and performance metrics such as throughput, packet loss ratio, accuracy, precision, and recall. These factors collectively contribute to the rationale behind choosing BSHNN as the preferred model for packet classification. The workflow of the BSHNN method is shown in Figure 3.  
Figure 3: Workflow of the BSHNN approach.
# Asymmetric Propagation Delay-Aware TDMA MAC Protocol for Mobile Underwater Acoustic Sensor Networks
## 3 Simulation Results and Analysis
### Performance Versus the Number of UN  
Figure 8 illustrates the \(CD\) and the \(CU\) of the proposed APD-TDMA and the BTB-TDMA. \(R\) and \(Vmax\) are fixed at 1500 m and 5 m/s, respectively. The horizontal axis is \(N\). Figure 8 shows that both the \(CD\) and the \(CU\) of the APD-TDMA (referred to as \(CD_{APD}\) and \(CU_{APD}\), respectively) outperforms those of the BTB-TDMA (referred to as \(CD_{BTB}\) and \(CU_{BTB}\)) regardless of \(N\). These performance gains of the APD-TDMA are achieved through the collision avoidance mechanism. Data packet collision at the SN may occur if the transmitted packet from a UN does not arrive at the scheduled time. This uncertainty of schedule is primarily due to the difference between the downlink propagation delay and the uplink propagation delay. In Figure 8a, the APD-TDMA reduces the average \(CD\) by 24% compared with that of the BTB-TDMA. In Figure 8b, the \(CU_{APD}\) decreases if \(N\) is larger than 25. The cycle duration is increased as \(N\) increases, and this increased cycle duration leads to the additional guard time of the APD-TDMA to avoid collision due to the asymmetric propagation delay, which degrades the \(CU_{APD}\) in the case of larger \(N\). On average, the \(CU_{APD}\) is greater than the \(CU_{BTB}\) by 31%.  
Figure 8: (**a**) The \(CD\) and (**b**) the \(CU\) of the APD-TDMA and the BTB-TDMA versus the number of nodes in which the network range is 1500 m and the maximum speed of nodes is 5 m/s.  
Figure 7: Performances of the BTB-TDMA1 and the BTB-TDMA2 versus the length of a time block at 5 nodes, 25 nodes, and 50 nodes when the network range is 1500 m and the maximum speed of nodes is 5 m/s. (**a**) The \(CR\) of the BTB-TDMA1; (**b**) the \(CD\) of the BTB-TDMA1 and the BTB-TDMA2; (**c**) the \(CU\) of the BTB-TDMA1 and the BTB-TDMA2.
## 3 Case Study  
In order to relate the proposed methodology to a real building, in this paper an apartment block located in the city of Cadiz, Spain was selected. It is one of the demonstration buildings on the European project RECO2 ST. It is a five-story residential building from the 1960s with a total of 28 apartments and a constructed area of 1873 m\({}^{2}\). At the time of writing this paper the building was uninhabited and the renovation project was underway to bring the building to NZEB consumption levels. The renovation of the building involves, among other aspects, the total replacement of the envelope, maintaining the structure of the building and the incorporation of facilities based on renewable energy.  
With this aim, under the RECO2 ST project the optimization of the new building envelope was studied with measures devoted to the reduction of heating and of cooling needs. This paper focuses on the improvements to the building envelope, which includes measures such as the addition of an insulation material on exterior walls and roofs on the outer layer of the original envelope and replacement of current windows with new more energy efficient ones. Moreover, to reduce the risk of overheating in the cooling season, the thermal insulation layer will be placed on the outside of the walls and ceilings since, as mentioned above, overheating can be reduced if the building has a high thermal inertia. Laying insulation on the outside helps to increase the thermal inertia of the building.